AIM: Create region by region analysis of male vs female for each age groups (0-4, 5-14, 0-14 and 15 plus) for all countries separated by region

library(dplyr)
library(tidyverse)
library(tidyr)
library(forecast)
Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 
library(fpp2)
── Attaching packages ─────────────────────────────────────────────────── fpp2 2.4 ──
✓ fma       2.4     ✓ expsmooth 2.3
── Conflicts ────────────────────────────────────────────────────── fpp2_conflicts ──
x magrittr::extract()   masks tidyr::extract()
x magrittr::set_names() masks purrr::set_names()
library(stringr)
library(magrittr)
library(ggplot2)

Organising the data by g_whoregion

grouped_2013 <- group_by(world_2013, g_whoregion) %>% filter(year == 2013) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2013)

grouped_2014 <- group_by(world_2013, g_whoregion) %>% filter(year == 2014) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2014)

grouped_2015 <- group_by(world_2013, g_whoregion) %>% filter(year == 2015) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2015)

grouped_2016 <- group_by(world_2013, g_whoregion) %>% filter(year == 2016) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2016)

grouped_2017 <- group_by(world_2013, g_whoregion) %>% filter(year == 2017) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2017)

grouped_2018 <- group_by(world_2013, g_whoregion) %>% filter(year == 2018) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2018)

grouped_2019 <- group_by(world_2013, g_whoregion) %>% filter(year == 2019) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2019)

grouped_2020 <- group_by(world_2013, g_whoregion) %>% filter(year == 2020) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2020)

bind_world <- rbind(grouped_2013, grouped_2014, grouped_2015, grouped_2016, grouped_2017, grouped_2018, grouped_2019, grouped_2020)

bind_world <- bind_world[, c(1, 10, 2:9)]

arrange_world <- arrange(bind_world, g_whoregion)

##Including columns for total values 

mutate_world <- mutate(arrange_world, "newrel_tot04" = newrel_m04 + newrel_f04, "newrel_tot514" = newrel_m514 + newrel_f514, "newrel_tot014" = newrel_m014 + newrel_f014, "newrel_tot15plus" = newrel_m15plus + newrel_f15plus)

perc_world <- mutate(mutate_world, "perc_014" = 100 * (newrel_tot014/(newrel_tot014 + newrel_tot15plus)), "perc_youngkids" = 100 * (newrel_tot04/newrel_tot014))

Creating graphs for male vs female for 0-4 by g_whoregion

afr_04 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(afr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AFR")
numeric(0)

amr_04 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(amr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AMR")
numeric(0)

emr_04 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(emr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EMR")
numeric(0)

eur_04 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(eur_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EUR")
numeric(0)

sea_04 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m04, newrel_f04) %>% 
  t() 
colnames(sea_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,60000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in SEA")
numeric(0)

wpr_04 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(wpr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in WPR")
numeric(0)

Creating graphs for notifications in 5-14 years by g_whoregion

afr_514 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,40000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-514 years in AFR")
numeric(0)

amr_514 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,4000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in AMR")
numeric(0)

emr_514 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(emr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EMR")
numeric(0)

eur_514 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m514, newrel_f514) %>% 
  t()
colnames(eur_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,5000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EUR")
numeric(0)

sea_514 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(sea_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,100000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in SEA")
numeric(0)

wpr_514 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m514, newrel_f514) %>% 
  t()
colnames(wpr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in WPR")
numeric(0)

Creating plot for 0-14

afr_014 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(afr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,70000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AFR")
numeric(0)

amr_014 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(amr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,6000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AMR")
numeric(0)

emr_014 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(emr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EMR")
numeric(0)

eur_014 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(eur_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,8000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EUR")
numeric(0)

sea_014 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(sea_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,150000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in SEA")
numeric(0)

wpr_014 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(wpr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in WPR")
numeric(0)

Creating plot for 15 plus

afr_15plus <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(afr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in AFR")
numeric(0)

amr_15plus <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t()
colnames(amr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,300000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15 plus years in AMR")
numeric(0)

emr_15plus <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(emr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EMR")
numeric(0)

eur_15plus <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(eur_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EUR")
numeric(0)

sea_15plus <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(sea_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,2000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in SEA")
numeric(0)

wpr_15plus <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(wpr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in WPR")
numeric(0)

Plotting total notifications over time

tot_world <- mutate(perc_world, "newrel_mtot" = newrel_m014 + newrel_m15plus, "newrel_ftot" = newrel_f014 + newrel_f15plus, "TOT" = newrel_mtot + newrel_ftot)

afr_tot <- tot_world %>% filter(g_whoregion == "AFR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(afr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AFR")
numeric(0)

amr_tot <- tot_world %>% filter(g_whoregion == "AMR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t()
colnames(amr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AMR")
numeric(0)

emr_tot <- tot_world %>% filter(g_whoregion == "EMR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(emr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EMR")
numeric(0)

eur_tot <- tot_world %>% filter(g_whoregion == "EUR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t()
colnames(eur_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EUR")
numeric(0)

sea_tot <- tot_world %>% filter(g_whoregion == "SEA") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(sea_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in SEA")
numeric(0)

wpr_tot <- tot_world %>% filter(g_whoregion == "WPR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(wpr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in WPR")
numeric(0)

##Attempt at creating a forecast 

filt_afr04 <- perc_world %>% filter(g_whoregion == "AFR") %>% filter(year != 2020)

ts_afr04 <- ts(filt_afr04[, c(3)], start = 2013, frequency = 1)

arima_afr04 <- auto.arima(ts_afr04, d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)

 ARIMA(0,1,0)                    : 111.1694
 ARIMA(0,1,0) with drift         : 114.7184
 ARIMA(0,1,1)                    : 115.9016
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 115.7354
 ARIMA(1,1,0) with drift         : Inf
 ARIMA(1,1,1)                    : 125.4818
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    
print(summary(arima_afr04))
Series: ts_afr04 
ARIMA(0,1,0) 

sigma^2 estimated as 3954567:  log likelihood=-54.08
AIC=110.17   AICc=111.17   BIC=109.96

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155
checkresiduals(arima_afr04)

    Ljung-Box test

data:  Residuals from ARIMA(0,1,0)
Q* = 5.0245, df = 3, p-value = 0.17

Model df: 0.   Total lags used: 3

fcst_afrm04 <- forecast(arima_afr04, h = 1)
autoplot(fcst)

sum_afrm04 <- print(summary(fcst))

Forecast method: ARIMA(0,1,0)

Model Information:
Series: ts_afr04 
ARIMA(0,1,0) 

sigma^2 estimated as 3954567:  log likelihood=-54.08
AIC=110.17   AICc=111.17   BIC=109.96

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155

Forecasts:
####Attempt at creating function to produce predictions 

out_2020 <- function(df = tot_world, region){
        df %>% filter(year != 2020) %>% filter(g_whoregion == region) %>% return() }

arima_TB <- function(df = tot_world, group) {
        data1 <- df %>% select(group) %>% ts(start = 2013, frequency = 1) %>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE) 
        print(summary(data1))
        FCST <- forecast(data1, h = 1)
        autoplot(FCST)
        print(summary(FCST))}

##AFRICA ESTIMATES 

afr_m04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m04") 
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(group)` instead of `group` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

 ARIMA(0,1,0)                    : 111.1694
 ARIMA(0,1,0) with drift         : 114.7184
 ARIMA(0,1,1)                    : 115.9016
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 115.7354
 ARIMA(1,1,0) with drift         : Inf
 ARIMA(1,1,1)                    : 125.4818
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 3954567:  log likelihood=-54.08
AIC=110.17   AICc=111.17   BIC=109.96

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 3954567:  log likelihood=-54.08
AIC=110.17   AICc=111.17   BIC=109.96

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155

Forecasts:
rownames(afr_m04) <- "afr_m04"

afr_f04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f04")

 ARIMA(0,1,0)                    : 111.1038
 ARIMA(0,1,0) with drift         : 114.5687
 ARIMA(0,1,1)                    : 116.0674
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 116.0417
 ARIMA(1,1,0) with drift         : 123.1058
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 3911516:  log likelihood=-54.05
AIC=110.1   AICc=111.1   BIC=109.9

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 808.0217 1831.046 1337.165 3.504448 6.329902 0.8588083 -0.4289628

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 3911516:  log likelihood=-54.05
AIC=110.1   AICc=111.1   BIC=109.9

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 808.0217 1831.046 1337.165 3.504448 6.329902 0.8588083 -0.4289628

Forecasts:
rownames(afr_f04) <- "afr_f04"

afr_m514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m514")

 ARIMA(0,1,0)                    : 117.6737
 ARIMA(0,1,0) with drift         : 121.1402
 ARIMA(0,1,1)                    : 122.6736
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 122.6736
 ARIMA(1,1,0) with drift         : 130.0079
 ARIMA(1,1,1)                    : 132.6046
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 131.7788
 ARIMA(2,1,0) with drift         : 159.9311
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 11691922:  log likelihood=-57.34
AIC=116.67   AICc=117.67   BIC=116.47

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1395.087 3165.699 2467.373 4.502111 8.326399 0.8582664 -0.3285392

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 11691922:  log likelihood=-57.34
AIC=116.67   AICc=117.67   BIC=116.47

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1395.087 3165.699 2467.373 4.502111 8.326399 0.8582664 -0.3285392

Forecasts:
rownames(afr_m514) <- "afr_m514"

afr_f514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f514")

 ARIMA(0,1,0)                    : 117.2334
 ARIMA(0,1,0) with drift         : 121.2201
 ARIMA(0,1,1)                    : 122.2328
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 122.2325
 ARIMA(1,1,0) with drift         : 130.5247
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 131.8107
 ARIMA(2,1,0) with drift         : 160.4149
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 10864754:  log likelihood=-57.12
AIC=116.23   AICc=117.23   BIC=116.03

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1117.231 3051.663 2274.088 3.379023 7.511132 0.8584703 -0.2394624

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 10864754:  log likelihood=-57.12
AIC=116.23   AICc=117.23   BIC=116.03

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1117.231 3051.663 2274.088 3.379023 7.511132 0.8584703 -0.2394624

Forecasts:
rownames(afr_f514) <- "afr_f514"

afr_m014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m014")

 ARIMA(0,1,0)                    : 121.8118
 ARIMA(0,1,0) with drift         : 125.8302
 ARIMA(0,1,1)                    : 126.7857
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 126.7703
 ARIMA(1,1,0) with drift         : 134.2116
 ARIMA(1,1,1)                    : 136.3851
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 135.8829
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 23303291:  log likelihood=-59.41
AIC=120.81   AICc=121.81   BIC=120.6

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1614.328 4469.256 3188.614 2.684558 5.574168 0.8590016 -0.3180155

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 23303291:  log likelihood=-59.41
AIC=120.81   AICc=121.81   BIC=120.6

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1614.328 4469.256 3188.614 2.684558 5.574168 0.8590016 -0.3180155

Forecasts:
rownames(afr_m014) <- "afr_m014"

afr_f014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f014")

 ARIMA(0,1,0)                    : 122.4939
 ARIMA(0,1,0) with drift         : 126.8435
 ARIMA(0,1,1)                    : 127.4896
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 127.488
 ARIMA(1,1,0) with drift         : 136.419
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 137.1726
 ARIMA(2,1,0) with drift         : 166.4068
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 26109056:  log likelihood=-59.75
AIC=121.49   AICc=122.49   BIC=121.29

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1410.503 4730.665 3787.646 2.301644 7.442093 0.8586494 -0.1756356

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 26109056:  log likelihood=-59.75
AIC=121.49   AICc=122.49   BIC=121.29

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1410.503 4730.665 3787.646 2.301644 7.442093 0.8586494 -0.1756356

Forecasts:
rownames(afr_f014) <- "afr_f014"

afr_m15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m15plus")

 ARIMA(0,1,0)                    : 147.8467
 ARIMA(0,1,0) with drift         : 151.7651
 ARIMA(0,1,1)                    : 152.7979
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 152.7548
 ARIMA(1,1,0) with drift         : 159.8714
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 161.2934
 ARIMA(2,1,0) with drift         : 189.8465
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 1.786e+09:  log likelihood=-72.42
AIC=146.85   AICc=147.85   BIC=146.64

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 14791.42 39126.57 26895.42 2.246912 4.075435 0.8596953 -0.3986632

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 1.786e+09:  log likelihood=-72.42
AIC=146.85   AICc=147.85   BIC=146.64

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 14791.42 39126.57 26895.42 2.246912 4.075435 0.8596953 -0.3986632

Forecasts:
rownames(afr_m15plus) <- "afr_m15plus"

afr_f15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f15plus")

 ARIMA(0,1,0)                    : 143.4497
 ARIMA(0,1,0) with drift         : 148.2692
 ARIMA(0,1,1)                    : 147.7646
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 157.6537
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 147.4938
 ARIMA(1,1,0) with drift         : 156.2684
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 157.1647
 ARIMA(2,1,0) with drift         : 186.183
 ARIMA(2,1,1)                    : 186.7743
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 858280286:  log likelihood=-70.22
AIC=142.45   AICc=143.45   BIC=142.24

Training set error measures:
                   ME     RMSE      MAE       MPE   MAPE     MASE       ACF1
Training set 4379.362 27123.22 20044.79 0.8601614 4.6618 0.859572 -0.4409411

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 858280286:  log likelihood=-70.22
AIC=142.45   AICc=143.45   BIC=142.24

Error measures:
                   ME     RMSE      MAE       MPE   MAPE     MASE       ACF1
Training set 4379.362 27123.22 20044.79 0.8601614 4.6618 0.859572 -0.4409411

Forecasts:
rownames(afr_f15plus) <- "afr_f15plus"

afr_estimates <- rbind(afr_m04, afr_f04, afr_m514, afr_f514, afr_m014, afr_f014, afr_m15plus, afr_f15plus)


#SEA predictions 
sea_m04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m04") 

 ARIMA(0,1,0)                    : 129.8095
 ARIMA(0,1,0) with drift         : 128.9832
 ARIMA(0,1,1)                    : 133.4638
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 133.3583
 ARIMA(1,1,0) with drift         : 138.4097
 ARIMA(1,1,1)                    : 143.2038
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 143.3582
 ARIMA(2,1,0) with drift         : 165.0954
 ARIMA(2,1,1)                    : 173.2038
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
         drift
      7409.833
s.e.  2361.681

sigma^2 estimated as 40157453:  log likelihood=-60.49
AIC=124.98   AICc=128.98   BIC=124.57

Training set error measures:
                     ME     RMSE     MAE       MPE     MAPE     MASE       ACF1
Training set -0.0466907 5355.735 4222.19 0.4100235 14.29603 0.569809 -0.2163359

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
         drift
      7409.833
s.e.  2361.681

sigma^2 estimated as 40157453:  log likelihood=-60.49
AIC=124.98   AICc=128.98   BIC=124.57

Error measures:
                     ME     RMSE     MAE       MPE     MAPE     MASE       ACF1
Training set -0.0466907 5355.735 4222.19 0.4100235 14.29603 0.569809 -0.2163359

Forecasts:
rownames(sea_m04) <- "sea_m04"

sea_f04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f04")

 ARIMA(0,1,0)                    : 126.2488
 ARIMA(0,1,0) with drift         : 125.9369
 ARIMA(0,1,1)                    : 130.0851
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 129.9836
 ARIMA(1,1,0) with drift         : 135.4326
 ARIMA(1,1,1)                    : 139.8509
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 139.9764
 ARIMA(2,1,0) with drift         : 162.569
 ARIMA(2,1,1)                    : 169.8484
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
        drift
      5355.00
s.e.  1832.19

sigma^2 estimated as 24169343:  log likelihood=-58.97
AIC=121.94   AICc=125.94   BIC=121.52

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set 0.1219997 4154.975 3249.836 0.6199827 13.78026 0.6068789 -0.1951322

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
        drift
      5355.00
s.e.  1832.19

sigma^2 estimated as 24169343:  log likelihood=-58.97
AIC=121.94   AICc=125.94   BIC=121.52

Error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set 0.1219997 4154.975 3249.836 0.6199827 13.78026 0.6068789 -0.1951322

Forecasts:
rownames(sea_f04) <- "sea_f04"

sea_m514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m514")

 ARIMA(0,1,0)                    : 138.7575
 ARIMA(0,1,0) with drift         : 141.1449
 ARIMA(0,1,1)                    : 143.4767
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 153.3661
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 143.3658
 ARIMA(1,1,0) with drift         : 151.0966
 ARIMA(1,1,1)                    : 153.2438
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 183.3643
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : 180.866
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 392626539:  log likelihood=-67.88
AIC=137.76   AICc=138.76   BIC=137.55

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 10092.84 18344.95 10092.84 16.02839 16.02839 0.8572872 -0.2886571

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 392626539:  log likelihood=-67.88
AIC=137.76   AICc=138.76   BIC=137.55

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 10092.84 18344.95 10092.84 16.02839 16.02839 0.8572872 -0.2886571

Forecasts:
rownames(sea_m514) <- "sea_m514"

sea_f514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f514")

 ARIMA(0,1,0)                    : 139.9886
 ARIMA(0,1,0) with drift         : 142.6269
 ARIMA(0,1,1)                    : 144.7946
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 154.2335
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 144.6597
 ARIMA(1,1,0) with drift         : 152.6186
 ARIMA(1,1,1)                    : 154.6524
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 184.2124
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 154.649
 ARIMA(2,1,0) with drift         : 182.54
 ARIMA(2,1,1)                    : 184.4943
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 482053005:  log likelihood=-68.49
AIC=138.99   AICc=139.99   BIC=138.78

Training set error measures:
                   ME     RMSE      MAE      MPE    MAPE      MASE       ACF1
Training set 10736.71 20327.03 11456.99 15.81939 16.8546 0.8572705 -0.2512842

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 482053005:  log likelihood=-68.49
AIC=138.99   AICc=139.99   BIC=138.78

Error measures:
                   ME     RMSE      MAE      MPE    MAPE      MASE       ACF1
Training set 10736.71 20327.03 11456.99 15.81939 16.8546 0.8572705 -0.2512842

Forecasts:
rownames(sea_f514) <- "sea_f514"

sea_m014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m014")

 ARIMA(0,1,0)                    : 143.1301
 ARIMA(0,1,0) with drift         : 144.5331
 ARIMA(0,1,1)                    : 147.643
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 157.5019
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 147.499
 ARIMA(1,1,0) with drift         : 154.2417
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 186.9908
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 157.4961
 ARIMA(2,1,0) with drift         : 183.4944
 ARIMA(2,1,1)                    : 187.4946
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 813725760:  log likelihood=-70.07
AIC=142.13   AICc=143.13   BIC=141.92

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 16421.31 26409.83 16421.31 17.32705 17.32705 0.8572857 -0.387646

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 813725760:  log likelihood=-70.07
AIC=142.13   AICc=143.13   BIC=141.92

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 16421.31 26409.83 16421.31 17.32705 17.32705 0.8572857 -0.387646

Forecasts:
rownames(sea_m014) <- "sea_m014"

sea_f014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f014")

 ARIMA(0,1,0)                    : 143.0538
 ARIMA(0,1,0) with drift         : 145.0229
 ARIMA(0,1,1)                    : 147.7415
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 157.2544
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 147.5565
 ARIMA(1,1,0) with drift         : 154.9293
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 187.2272
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 157.5482
 ARIMA(2,1,0) with drift         : 184.6836
 ARIMA(2,1,1)                    : 187.3948
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 803450152:  log likelihood=-70.03
AIC=142.05   AICc=143.05   BIC=141.85

Training set error measures:
                  ME     RMSE     MAE      MPE     MAPE      MASE       ACF1
Training set 15302.9 26242.55 15302.9 16.59731 16.59731 0.8572895 -0.3224274

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 803450152:  log likelihood=-70.03
AIC=142.05   AICc=143.05   BIC=141.85

Error measures:
                  ME     RMSE     MAE      MPE     MAPE      MASE       ACF1
Training set 15302.9 26242.55 15302.9 16.59731 16.59731 0.8572895 -0.3224274

Forecasts:
rownames(sea_f014) <- "sea_f014"

sea_m15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m15plus")

 ARIMA(0,1,0)                    : 176.6742
 ARIMA(0,1,0) with drift         : 179.3839
 ARIMA(0,1,1)                    : 181.6143
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 191.2782
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 181.5536
 ARIMA(1,1,0) with drift         : 189.2337
 ARIMA(1,1,1)                    : 191.3233
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 221.1653
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 191.4064
 ARIMA(2,1,0) with drift         : 219.2186
 ARIMA(2,1,1)                    : 221.2795
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 2.18e+11:  log likelihood=-86.84
AIC=175.67   AICc=176.67   BIC=175.47

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 225490.8 432281.4 237748.2 14.63527 15.40864 0.8573248 -0.3031529

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 2.18e+11:  log likelihood=-86.84
AIC=175.67   AICc=176.67   BIC=175.47

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 225490.8 432281.4 237748.2 14.63527 15.40864 0.8573248 -0.3031529

Forecasts:
rownames(sea_m15plus) <- "sea_m15plus"

sea_f15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f15plus")

 ARIMA(0,1,0)                    : 168.8186
 ARIMA(0,1,0) with drift         : 170.5037
 ARIMA(0,1,1)                    : 173.6346
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 173.459
 ARIMA(1,1,0) with drift         : 180.1729
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 183.2908
 ARIMA(2,1,0) with drift         : 210.0185
 ARIMA(2,1,1)                    : 213.0905
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 5.887e+10:  log likelihood=-82.91
AIC=167.82   AICc=168.82   BIC=167.61

Training set error measures:
                 ME     RMSE    MAE      MPE     MAPE      MASE       ACF1
Training set 135527 224627.7 135527 15.54276 15.54276 0.8573545 -0.3868371

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 5.887e+10:  log likelihood=-82.91
AIC=167.82   AICc=168.82   BIC=167.61

Error measures:
                 ME     RMSE    MAE      MPE     MAPE      MASE       ACF1
Training set 135527 224627.7 135527 15.54276 15.54276 0.8573545 -0.3868371

Forecasts:
rownames(sea_f15plus) <- "sea_f15plus"

sea_estimates <- rbind(sea_m04, sea_f04, sea_m514, sea_f514, sea_m014, sea_f014, sea_m15plus, sea_f15plus)

#WPR estimates 

wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04") 

 ARIMA(0,1,0)                    : 117.987
 ARIMA(0,1,0) with drift         : 120.6596
 ARIMA(0,1,1)                    : 122.6039
 ARIMA(0,1,1) with drift         : 130.6566
 ARIMA(0,1,2)                    : 132.1259
 ARIMA(0,1,2) with drift         : 160.3447
 ARIMA(1,1,0)                    : 122.4011
 ARIMA(1,1,0) with drift         : 130.6558
 ARIMA(1,1,1)                    : 132.1779
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 161.9353
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 131.972
 ARIMA(2,1,0) with drift         : 160.5433
 ARIMA(2,1,1)                    : 161.9623
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 12318696:  log likelihood=-57.49
AIC=116.99   AICc=117.99   BIC=116.78

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 12318696:  log likelihood=-57.49
AIC=116.99   AICc=117.99   BIC=116.78

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836

Forecasts:
rownames(wpr_m04) <- "wpr_m04"

wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")

 ARIMA(0,1,0)                    : 115.7528
 ARIMA(0,1,0) with drift         : 118.5332
 ARIMA(0,1,1)                    : 120.4465
 ARIMA(0,1,1) with drift         : 128.5172
 ARIMA(0,1,2)                    : 130.0029
 ARIMA(0,1,2) with drift         : 158.2683
 ARIMA(1,1,0)                    : 120.2961
 ARIMA(1,1,0) with drift         : 128.5132
 ARIMA(1,1,1)                    : 130.083
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 159.8347
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 129.8787
 ARIMA(2,1,0) with drift         : 158.4132
 ARIMA(2,1,1)                    : 159.8769
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 8488853:  log likelihood=-56.38
AIC=114.75   AICc=115.75   BIC=114.54

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 8488853:  log likelihood=-56.38
AIC=114.75   AICc=115.75   BIC=114.54

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096

Forecasts:
rownames(wpr_f04) <- "wpr_f04"

wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")

 ARIMA(0,1,0)                    : 121.504
 ARIMA(0,1,0) with drift         : 123.8299
 ARIMA(0,1,1)                    : 126.3435
 ARIMA(0,1,1) with drift         : 133.7516
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 126.0875
 ARIMA(1,1,0) with drift         : 133.6501
 ARIMA(1,1,1)                    : 135.4448
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 22137609:  log likelihood=-59.25
AIC=120.5   AICc=121.5   BIC=120.3

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 22137609:  log likelihood=-59.25
AIC=120.5   AICc=121.5   BIC=120.3

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211

Forecasts:
rownames(wpr_m514) <- "wpr_m514"

wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")

 ARIMA(0,1,0)                    : 119.8234
 ARIMA(0,1,0) with drift         : 122.1114
 ARIMA(0,1,1)                    : 124.7307
 ARIMA(0,1,1) with drift         : 131.9174
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 124.5719
 ARIMA(1,1,0) with drift         : 131.6532
 ARIMA(1,1,1)                    : 133.8761
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 16729524:  log likelihood=-58.41
AIC=118.82   AICc=119.82   BIC=118.62

Training set error measures:
                   ME     RMSE    MAE      MPE    MAPE      MASE       ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 16729524:  log likelihood=-58.41
AIC=118.82   AICc=119.82   BIC=118.62

Error measures:
                   ME     RMSE    MAE      MPE    MAPE      MASE       ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534

Forecasts:
rownames(wpr_f514) <- "wpr_f514"

wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")

 ARIMA(0,1,0)                    : 127.329
 ARIMA(0,1,0) with drift         : 129.3522
 ARIMA(0,1,1)                    : 132.0091
 ARIMA(0,1,1) with drift         : 139.3173
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 131.6732
 ARIMA(1,1,0) with drift         : 139.2927
 ARIMA(1,1,1)                    : 141.1479
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 140.1957
 ARIMA(2,1,0) with drift         : 168.6569
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 58446545:  log likelihood=-62.16
AIC=126.33   AICc=127.33   BIC=126.12

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 58446545:  log likelihood=-62.16
AIC=126.33   AICc=127.33   BIC=126.12

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907

Forecasts:
rownames(wpr_m014) <- "wpr_m014"

wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")

 ARIMA(0,1,0)                    : 125.4245
 ARIMA(0,1,0) with drift         : 127.5424
 ARIMA(0,1,1)                    : 130.1948
 ARIMA(0,1,1) with drift         : 137.4582
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 129.9333
 ARIMA(1,1,0) with drift         : 137.3915
 ARIMA(1,1,1)                    : 139.3634
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 138.0696
 ARIMA(2,1,0) with drift         : 166.547
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 42550393:  log likelihood=-61.21
AIC=124.42   AICc=125.42   BIC=124.22

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 42550393:  log likelihood=-61.21
AIC=124.42   AICc=125.42   BIC=124.22

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519

Forecasts:
rownames(wpr_f014) <- "wpr_f014"

wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")

 ARIMA(0,1,0)                    : 151.1578
 ARIMA(0,1,0) with drift         : 155.1099
 ARIMA(0,1,1)                    : 156.1526
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 156.1532
 ARIMA(1,1,0) with drift         : 163.9579
 ARIMA(1,1,1)                    : 166.1515
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 166.0983
 ARIMA(2,1,0) with drift         : 193.0796
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 3.101e+09:  log likelihood=-74.08
AIC=150.16   AICc=151.16   BIC=149.95

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 3.101e+09:  log likelihood=-74.08
AIC=150.16   AICc=151.16   BIC=149.95

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366

Forecasts:
rownames(wpr_m15plus) <- "wpr_m15plus"

wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")

 ARIMA(0,1,0)                    : 143.6556
 ARIMA(0,1,0) with drift         : 146.9362
 ARIMA(0,1,1)                    : 148.6553
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 148.6553
 ARIMA(1,1,0) with drift         : 154.5376
 ARIMA(1,1,1)                    : 158.6553
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 158.6417
 ARIMA(2,1,0) with drift         : 182.9765
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 888234487:  log likelihood=-70.33
AIC=142.66   AICc=143.66   BIC=142.45

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 888234487:  log likelihood=-70.33
AIC=142.66   AICc=143.66   BIC=142.45

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906

Forecasts:
rownames(wpr_f15plus) <- "wpr_f15plus"

wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)

#EUR Estimates

eur_m04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m04") 

 ARIMA(0,1,0)                    : 78.75381
 ARIMA(0,1,0) with drift         : 82.12951
 ARIMA(0,1,1)                    : 83.64837
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 93.2627
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 83.6904
 ARIMA(1,1,0) with drift         : 92.06954
 ARIMA(1,1,1)                    : 93.5146
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 123.2595
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 17815:  log likelihood=-37.88
AIC=77.75   AICc=78.75   BIC=77.55

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -55.44929 123.5715 83.40786 -3.641441 5.183049 0.8598748 -0.1116328

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 17815:  log likelihood=-37.88
AIC=77.75   AICc=78.75   BIC=77.55

Error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -55.44929 123.5715 83.40786 -3.641441 5.183049 0.8598748 -0.1116328

Forecasts:
rownames(eur_m04) <- "eur_m04"

eur_f04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f04")

 ARIMA(0,1,0)                    : 76.68985
 ARIMA(0,1,0) with drift         : 80.33647
 ARIMA(0,1,1)                    : 81.58578
 ARIMA(0,1,1) with drift         : 90.27588
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 81.57517
 ARIMA(1,1,0) with drift         : 90.28316
 ARIMA(1,1,1)                    : 91.57496
 ARIMA(1,1,1) with drift         : 120.2759
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : 120.15
 ARIMA(2,1,1)                    : 121.4982
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 12630:  log likelihood=-36.84
AIC=75.69   AICc=76.69   BIC=75.48

Training set error measures:
                    ME    RMSE      MAE       MPE     MAPE      MASE        ACF1
Training set -43.06114 104.045 79.22457 -3.311926 5.619945 0.8595794 0.004291823

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 12630:  log likelihood=-36.84
AIC=75.69   AICc=76.69   BIC=75.48

Error measures:
                    ME    RMSE      MAE       MPE     MAPE      MASE        ACF1
Training set -43.06114 104.045 79.22457 -3.311926 5.619945 0.8595794 0.004291823

Forecasts:
rownames(eur_f04) <- "eur_f04"

eur_m514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m514")

 ARIMA(0,1,0)                    : 85.509
 ARIMA(0,1,0) with drift         : 86.43678
 ARIMA(0,1,1)                    : Inf
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 90.11173
 ARIMA(1,1,0) with drift         : 95.54597
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 100.0673
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 54922:  log likelihood=-41.25
AIC=84.51   AICc=85.51   BIC=84.3

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -140.4223 216.9705 141.5777 -4.173495 4.202066 0.8606548 -0.4670638

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 54922:  log likelihood=-41.25
AIC=84.51   AICc=85.51   BIC=84.3

Error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -140.4223 216.9705 141.5777 -4.173495 4.202066 0.8606548 -0.4670638

Forecasts:
rownames(eur_m514) <- "eur_m514"

eur_f514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f514")

 ARIMA(0,1,0)                    : 84.80427
 ARIMA(0,1,0) with drift         : 86.44661
 ARIMA(0,1,1)                    : 89.6705
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 89.57782
 ARIMA(1,1,0) with drift         : 94.21642
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 99.14526
 ARIMA(2,1,0) with drift         : 121.6276
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 48836:  log likelihood=-40.9
AIC=83.8   AICc=84.8   BIC=83.6

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE     MASE       ACF1
Training set -123.4541 204.5953 147.1173 -3.853962 4.643237 0.860335 -0.5094926

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 48836:  log likelihood=-40.9
AIC=83.8   AICc=84.8   BIC=83.6

Error measures:
                    ME     RMSE      MAE       MPE     MAPE     MASE       ACF1
Training set -123.4541 204.5953 147.1173 -3.853962 4.643237 0.860335 -0.5094926

Forecasts:
rownames(eur_f514) <- "eur_f514"

eur_m014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m014")

 ARIMA(0,1,0)                    : 87.90563
 ARIMA(0,1,0) with drift         : 87.17066
 ARIMA(0,1,1)                    : Inf
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 91.3642
 ARIMA(1,1,0) with drift         : 96.87938
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 101.202
 ARIMA(2,1,0) with drift         : 116.0782
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      -224.5000
s.e.    72.4391

sigma^2 estimated as 37788:  log likelihood=-39.59
AIC=83.17   AICc=87.17   BIC=82.75

Training set error measures:
                    ME     RMSE      MAE         MPE     MAPE      MASE       ACF1
Training set 0.8747853 164.2911 147.3034 0.003111067 2.892103 0.6561397 -0.1918559

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      -224.5000
s.e.    72.4391

sigma^2 estimated as 37788:  log likelihood=-39.59
AIC=83.17   AICc=87.17   BIC=82.75

Error measures:
                    ME     RMSE      MAE         MPE     MAPE      MASE       ACF1
Training set 0.8747853 164.2911 147.3034 0.003111067 2.892103 0.6561397 -0.1918559

Forecasts:
rownames(eur_m014) <- "eur_m014"

eur_f014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f014")

 ARIMA(0,1,0)                    : 87.89843
 ARIMA(0,1,0) with drift         : 89.26901
 ARIMA(0,1,1)                    : 92.88049
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 102.6458
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 92.86608
 ARIMA(1,1,0) with drift         : 97.28552
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 132.2977
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 102.1516
 ARIMA(2,1,0) with drift         : 126.8821
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 81791:  log likelihood=-42.45
AIC=86.9   AICc=87.9   BIC=86.69

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -164.3724 264.7762 197.3419 -3.582997 4.247259 0.8605023 -0.5893928

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 81791:  log likelihood=-42.45
AIC=86.9   AICc=87.9   BIC=86.69

Error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -164.3724 264.7762 197.3419 -3.582997 4.247259 0.8605023 -0.5893928

Forecasts:
rownames(eur_f014) <- "eur_f014"

eur_m15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m15plus")

 ARIMA(0,1,0)                    : 125.9029
 ARIMA(0,1,0) with drift         : 120.3483
 ARIMA(0,1,1)                    : Inf
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : Inf
 ARIMA(1,1,0) with drift         : 128.3634
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 129.0743
 ARIMA(2,1,0) with drift         : 155.9324
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      -6176.333
s.e.   1150.038

sigma^2 estimated as 9528860:  log likelihood=-56.17
AIC=116.35   AICc=120.35   BIC=115.93

Training set error measures:
                   ME     RMSE      MAE         MPE     MAPE      MASE      ACF1
Training set 25.60432 2608.894 2185.795 -0.08003539 1.376032 0.3538985 0.4138192

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      -6176.333
s.e.   1150.038

sigma^2 estimated as 9528860:  log likelihood=-56.17
AIC=116.35   AICc=120.35   BIC=115.93

Error measures:
                   ME     RMSE      MAE         MPE     MAPE      MASE      ACF1
Training set 25.60432 2608.894 2185.795 -0.08003539 1.376032 0.3538985 0.4138192

Forecasts:
rownames(eur_m15plus) <- "eur_m15plus"

eur_f15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f15plus")

 ARIMA(0,1,0)                    : 116.7389
 ARIMA(0,1,0) with drift         : 104.0178
 ARIMA(0,1,1)                    : Inf
 ARIMA(0,1,1) with drift         : 113.7773
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : Inf
 ARIMA(1,1,0) with drift         : 113.975
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 148.6417
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : 143.0256
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
           drift
      -3079.5000
s.e.    294.9107

sigma^2 estimated as 627839:  log likelihood=-48.01
AIC=100.02   AICc=104.02   BIC=99.6

Training set error measures:
                   ME     RMSE      MAE         MPE     MAPE      MASE      ACF1
Training set 12.97978 669.6689 474.9798 -0.01880938 0.588106 0.1542393 0.0977167

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
           drift
      -3079.5000
s.e.    294.9107

sigma^2 estimated as 627839:  log likelihood=-48.01
AIC=100.02   AICc=104.02   BIC=99.6

Error measures:
                   ME     RMSE      MAE         MPE     MAPE      MASE      ACF1
Training set 12.97978 669.6689 474.9798 -0.01880938 0.588106 0.1542393 0.0977167

Forecasts:
rownames(eur_f15plus) <- "eur_f15plus"

eur_estimates <- rbind(eur_m04, eur_f04, eur_m514, eur_f514, eur_m014, eur_f014, eur_m15plus, eur_f15plus)

#AMR Estimates

emr_m04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m04") 

 ARIMA(0,1,0)                    : 110.5715
 ARIMA(0,1,0) with drift         : 106.0002
 ARIMA(0,1,1)                    : 111.9022
 ARIMA(0,1,1) with drift         : 115.5503
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 107.6886
 ARIMA(1,1,0) with drift         : 115.508
 ARIMA(1,1,1)                    : 117.6708
 ARIMA(1,1,1) with drift         : 145.4466
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 117.6505
 ARIMA(2,1,0) with drift         : 145.242
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      1689.1667
s.e.   347.8854

sigma^2 estimated as 871360:  log likelihood=-49
AIC=102   AICc=106   BIC=101.58

Training set error measures:
                   ME     RMSE      MAE       MPE     MAPE      MASE      ACF1
Training set 0.532833 788.9232 524.0566 0.6179599 3.986697 0.3102457 0.2009148

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      1689.1667
s.e.   347.8854

sigma^2 estimated as 871360:  log likelihood=-49
AIC=102   AICc=106   BIC=101.58

Error measures:
                   ME     RMSE      MAE       MPE     MAPE      MASE      ACF1
Training set 0.532833 788.9232 524.0566 0.6179599 3.986697 0.3102457 0.2009148

Forecasts:
rownames(emr_m04) <- "emr_m04"

emr_f04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f04")

 ARIMA(0,1,0)                    : 107.6113
 ARIMA(0,1,0) with drift         : 103.7642
 ARIMA(0,1,1)                    : 110.6912
 ARIMA(0,1,1) with drift         : 113.6269
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 107.7608
 ARIMA(1,1,0) with drift         : 113.5916
 ARIMA(1,1,1)                    : 117.0607
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 116.1464
 ARIMA(2,1,0) with drift         : 143.3858
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      1298.1667
s.e.   288.7444

sigma^2 estimated as 600277:  log likelihood=-47.88
AIC=99.76   AICc=103.76   BIC=99.35

Training set error measures:
                    ME    RMSE     MAE       MPE     MAPE      MASE       ACF1
Training set 0.4144046 654.805 447.462 0.7599193 4.915919 0.3446877 -0.1203015

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      1298.1667
s.e.   288.7444

sigma^2 estimated as 600277:  log likelihood=-47.88
AIC=99.76   AICc=103.76   BIC=99.35

Error measures:
                    ME    RMSE     MAE       MPE     MAPE      MASE       ACF1
Training set 0.4144046 654.805 447.462 0.7599193 4.915919 0.3446877 -0.1203015

Forecasts:
rownames(emr_f04) <- "emr_f04"

emr_m514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m514")

 ARIMA(0,1,0)                    : 106.2175
 ARIMA(0,1,0) with drift         : 106.5495
 ARIMA(0,1,1)                    : Inf
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 108.4115
 ARIMA(1,1,0) with drift         : 116.5418
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 117.5946
 ARIMA(2,1,0) with drift         : 144.3844
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 1732475:  log likelihood=-51.61
AIC=105.22   AICc=106.22   BIC=105.01

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 831.4019 1218.597 893.9733 5.034561 5.531396 0.8589015 0.2435649

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 1732475:  log likelihood=-51.61
AIC=105.22   AICc=106.22   BIC=105.01

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 831.4019 1218.597 893.9733 5.034561 5.531396 0.8589015 0.2435649

Forecasts:
rownames(emr_m514) <- "emr_m514"

emr_f514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f514")

 ARIMA(0,1,0)                    : 106.7125
 ARIMA(0,1,0) with drift         : 109.5098
 ARIMA(0,1,1)                    : Inf
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 110.6453
 ARIMA(1,1,0) with drift         : 119.4974
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 119.929
 ARIMA(2,1,0) with drift         : 148.2944
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 1881467:  log likelihood=-51.86
AIC=105.71   AICc=106.71   BIC=105.5

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 654.1529 1269.916 998.7243 3.163926 5.159425 0.8592409 0.1308076

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 1881467:  log likelihood=-51.86
AIC=105.71   AICc=106.71   BIC=105.5

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 654.1529 1269.916 998.7243 3.163926 5.159425 0.8592409 0.1308076

Forecasts:
rownames(emr_f514) <- "emr_f514"

emr_m014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m014")

 ARIMA(0,1,0)                    : 117.0617
 ARIMA(0,1,0) with drift         : 115.5705
 ARIMA(0,1,1)                    : 118.5877
 ARIMA(0,1,1) with drift         : 125.0624
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 116.8657
 ARIMA(1,1,0) with drift         : 125.2341
 ARIMA(1,1,1)                    : 126.7096
 ARIMA(1,1,1) with drift         : 155.0521
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 126.5296
 ARIMA(2,1,0) with drift         : 154.4364
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      2641.8333
s.e.   772.3193

sigma^2 estimated as 4294599:  log likelihood=-53.79
AIC=111.57   AICc=115.57   BIC=111.15

Training set error measures:
                   ME     RMSE      MAE       MPE     MAPE      MASE      ACF1
Training set 2.240166 1751.448 1276.383 0.1088444 4.518639 0.4637739 0.1764573

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      2641.8333
s.e.   772.3193

sigma^2 estimated as 4294599:  log likelihood=-53.79
AIC=111.57   AICc=115.57   BIC=111.15

Error measures:
                   ME     RMSE      MAE       MPE     MAPE      MASE      ACF1
Training set 2.240166 1751.448 1276.383 0.1088444 4.518639 0.4637739 0.1764573

Forecasts:
rownames(emr_m014) <- "emr_m014"

emr_f014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f014")

 ARIMA(0,1,0)                    : 114.8804
 ARIMA(0,1,0) with drift         : 114.8509
 ARIMA(0,1,1)                    : 116.6524
 ARIMA(0,1,1) with drift         : 124.3216
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 115.7161
 ARIMA(1,1,0) with drift         : 124.5359
 ARIMA(1,1,1)                    : 125.4571
 ARIMA(1,1,1) with drift         : 154.3157
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 125.0684
 ARIMA(2,1,0) with drift         : 153.5805
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      2041.0000
s.e.   727.3703

sigma^2 estimated as 3809285:  log likelihood=-53.43
AIC=110.85   AICc=114.85   BIC=110.43

Training set error measures:
                   ME    RMSE      MAE        MPE     MAPE      MASE     ACF1
Training set 2.761856 1649.52 1225.619 0.05313471 4.220174 0.5291965 0.162671

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      2041.0000
s.e.   727.3703

sigma^2 estimated as 3809285:  log likelihood=-53.43
AIC=110.85   AICc=114.85   BIC=110.43

Error measures:
                   ME    RMSE      MAE        MPE     MAPE      MASE     ACF1
Training set 2.761856 1649.52 1225.619 0.05313471 4.220174 0.5291965 0.162671

Forecasts:
rownames(emr_f014) <- "emr_f014"

emr_m15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m15plus")

 ARIMA(0,1,0)                    : 133.2224
 ARIMA(0,1,0) with drift         : 136.4537
 ARIMA(0,1,1)                    : 138.0252
 ARIMA(0,1,1) with drift         : 146.4274
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : 176.2163
 ARIMA(1,1,0)                    : 137.8248
 ARIMA(1,1,0) with drift         : 146.4201
 ARIMA(1,1,1)                    : 147.6019
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 147.0673
 ARIMA(2,1,0) with drift         : 176.3108
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 156081396:  log likelihood=-65.11
AIC=132.22   AICc=133.22   BIC=132.01

Training set error measures:
                   ME     RMSE      MAE      MPE    MAPE      MASE      ACF1
Training set 5437.489 11566.51 8570.917 2.486889 3.87156 0.8598432 -0.120371

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 156081396:  log likelihood=-65.11
AIC=132.22   AICc=133.22   BIC=132.01

Error measures:
                   ME     RMSE      MAE      MPE    MAPE      MASE      ACF1
Training set 5437.489 11566.51 8570.917 2.486889 3.87156 0.8598432 -0.120371

Forecasts:
rownames(emr_m15plus) <- "emr_m15plus"

emr_f15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f15plus")

 ARIMA(0,1,0)                    : 131.2118
 ARIMA(0,1,0) with drift         : 135.5512
 ARIMA(0,1,1)                    : 136.2115
 ARIMA(0,1,1) with drift         : 145.4442
 ARIMA(0,1,2)                    : 145.3951
 ARIMA(0,1,2) with drift         : 175.3264
 ARIMA(1,1,0)                    : 136.2112
 ARIMA(1,1,0) with drift         : 145.4197
 ARIMA(1,1,1)                    : 146.1754
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 145.6273
 ARIMA(2,1,0) with drift         : 175.2787
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 111641770:  log likelihood=-64.11
AIC=130.21   AICc=131.21   BIC=130

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 2949.988 9782.277 7055.131 1.426539 3.469009 0.8602944 -0.1560189

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 111641770:  log likelihood=-64.11
AIC=130.21   AICc=131.21   BIC=130

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 2949.988 9782.277 7055.131 1.426539 3.469009 0.8602944 -0.1560189

Forecasts:
rownames(emr_f15plus) <- "emr_f15plus"

emr_estimates <- rbind(emr_m04, emr_f04, emr_m514, emr_f514, emr_m014, emr_f014, emr_m15plus, emr_f15plus)

#WPR Estimates

wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04") 

 ARIMA(0,1,0)                    : 117.987
 ARIMA(0,1,0) with drift         : 120.6596
 ARIMA(0,1,1)                    : 122.6039
 ARIMA(0,1,1) with drift         : 130.6566
 ARIMA(0,1,2)                    : 132.1259
 ARIMA(0,1,2) with drift         : 160.3447
 ARIMA(1,1,0)                    : 122.4011
 ARIMA(1,1,0) with drift         : 130.6558
 ARIMA(1,1,1)                    : 132.1779
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 161.9353
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 131.972
 ARIMA(2,1,0) with drift         : 160.5433
 ARIMA(2,1,1)                    : 161.9623
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 12318696:  log likelihood=-57.49
AIC=116.99   AICc=117.99   BIC=116.78

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 12318696:  log likelihood=-57.49
AIC=116.99   AICc=117.99   BIC=116.78

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836

Forecasts:
rownames(wpr_m04) <- "wpr_m04"

wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")

 ARIMA(0,1,0)                    : 115.7528
 ARIMA(0,1,0) with drift         : 118.5332
 ARIMA(0,1,1)                    : 120.4465
 ARIMA(0,1,1) with drift         : 128.5172
 ARIMA(0,1,2)                    : 130.0029
 ARIMA(0,1,2) with drift         : 158.2683
 ARIMA(1,1,0)                    : 120.2961
 ARIMA(1,1,0) with drift         : 128.5132
 ARIMA(1,1,1)                    : 130.083
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 159.8347
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 129.8787
 ARIMA(2,1,0) with drift         : 158.4132
 ARIMA(2,1,1)                    : 159.8769
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 8488853:  log likelihood=-56.38
AIC=114.75   AICc=115.75   BIC=114.54

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 8488853:  log likelihood=-56.38
AIC=114.75   AICc=115.75   BIC=114.54

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096

Forecasts:
rownames(wpr_f04) <- "wpr_f04"

wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")

 ARIMA(0,1,0)                    : 121.504
 ARIMA(0,1,0) with drift         : 123.8299
 ARIMA(0,1,1)                    : 126.3435
 ARIMA(0,1,1) with drift         : 133.7516
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 126.0875
 ARIMA(1,1,0) with drift         : 133.6501
 ARIMA(1,1,1)                    : 135.4448
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 22137609:  log likelihood=-59.25
AIC=120.5   AICc=121.5   BIC=120.3

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 22137609:  log likelihood=-59.25
AIC=120.5   AICc=121.5   BIC=120.3

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE      ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211

Forecasts:
rownames(wpr_m514) <- "wpr_m514"

wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")

 ARIMA(0,1,0)                    : 119.8234
 ARIMA(0,1,0) with drift         : 122.1114
 ARIMA(0,1,1)                    : 124.7307
 ARIMA(0,1,1) with drift         : 131.9174
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 124.5719
 ARIMA(1,1,0) with drift         : 131.6532
 ARIMA(1,1,1)                    : 133.8761
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 16729524:  log likelihood=-58.41
AIC=118.82   AICc=119.82   BIC=118.62

Training set error measures:
                   ME     RMSE    MAE      MPE    MAPE      MASE       ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 16729524:  log likelihood=-58.41
AIC=118.82   AICc=119.82   BIC=118.62

Error measures:
                   ME     RMSE    MAE      MPE    MAPE      MASE       ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534

Forecasts:
rownames(wpr_f514) <- "wpr_f514"

wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")

 ARIMA(0,1,0)                    : 127.329
 ARIMA(0,1,0) with drift         : 129.3522
 ARIMA(0,1,1)                    : 132.0091
 ARIMA(0,1,1) with drift         : 139.3173
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 131.6732
 ARIMA(1,1,0) with drift         : 139.2927
 ARIMA(1,1,1)                    : 141.1479
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 140.1957
 ARIMA(2,1,0) with drift         : 168.6569
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 58446545:  log likelihood=-62.16
AIC=126.33   AICc=127.33   BIC=126.12

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 58446545:  log likelihood=-62.16
AIC=126.33   AICc=127.33   BIC=126.12

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907

Forecasts:
rownames(wpr_m014) <- "wpr_m014"

wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")

 ARIMA(0,1,0)                    : 125.4245
 ARIMA(0,1,0) with drift         : 127.5424
 ARIMA(0,1,1)                    : 130.1948
 ARIMA(0,1,1) with drift         : 137.4582
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 129.9333
 ARIMA(1,1,0) with drift         : 137.3915
 ARIMA(1,1,1)                    : 139.3634
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 138.0696
 ARIMA(2,1,0) with drift         : 166.547
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 42550393:  log likelihood=-61.21
AIC=124.42   AICc=125.42   BIC=124.22

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 42550393:  log likelihood=-61.21
AIC=124.42   AICc=125.42   BIC=124.22

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519

Forecasts:
rownames(wpr_f014) <- "wpr_f014"

wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")

 ARIMA(0,1,0)                    : 151.1578
 ARIMA(0,1,0) with drift         : 155.1099
 ARIMA(0,1,1)                    : 156.1526
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 156.1532
 ARIMA(1,1,0) with drift         : 163.9579
 ARIMA(1,1,1)                    : 166.1515
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 166.0983
 ARIMA(2,1,0) with drift         : 193.0796
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 3.101e+09:  log likelihood=-74.08
AIC=150.16   AICc=151.16   BIC=149.95

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 3.101e+09:  log likelihood=-74.08
AIC=150.16   AICc=151.16   BIC=149.95

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366

Forecasts:
rownames(wpr_m15plus) <- "wpr_m15plus"

wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")

 ARIMA(0,1,0)                    : 143.6556
 ARIMA(0,1,0) with drift         : 146.9362
 ARIMA(0,1,1)                    : 148.6553
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 148.6553
 ARIMA(1,1,0) with drift         : 154.5376
 ARIMA(1,1,1)                    : 158.6553
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 158.6417
 ARIMA(2,1,0) with drift         : 182.9765
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 888234487:  log likelihood=-70.33
AIC=142.66   AICc=143.66   BIC=142.45

Training set error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 888234487:  log likelihood=-70.33
AIC=142.66   AICc=143.66   BIC=142.45

Error measures:
                   ME     RMSE      MAE      MPE     MAPE      MASE       ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906

Forecasts:
rownames(wpr_f15plus) <- "wpr_f15plus"

wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)

#AMR Estimates

amr_m04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m04") 

 ARIMA(0,1,0)                    : 81.20312
 ARIMA(0,1,0) with drift         : 85.62773
 ARIMA(0,1,1)                    : Inf
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 83.36317
 ARIMA(1,1,0) with drift         : 93.10285
 ARIMA(1,1,1)                    : Inf
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 91.99538
 ARIMA(2,1,0) with drift         : 121.9132
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 26796:  log likelihood=-39.1
AIC=80.2   AICc=81.2   BIC=79.99

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -42.09157 151.5522 120.1941 -2.150665 5.588892 0.8595529 -0.4510363

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 26796:  log likelihood=-39.1
AIC=80.2   AICc=81.2   BIC=79.99

Error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -42.09157 151.5522 120.1941 -2.150665 5.588892 0.8595529 -0.4510363

Forecasts:
rownames(amr_m04) <- "amr_m04"

amr_f04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f04")

 ARIMA(0,1,0)                    : 79.78439
 ARIMA(0,1,0) with drift         : 84.32867
 ARIMA(0,1,1)                    : 81.41097
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 80.0876
 ARIMA(1,1,0) with drift         : 89.04987
 ARIMA(1,1,1)                    : 89.05019
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 88.27209
 ARIMA(2,1,0) with drift         : 116.0278
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 21153:  log likelihood=-38.39
AIC=78.78   AICc=79.78   BIC=78.58

Training set error measures:
                    ME    RMSE      MAE       MPE    MAPE      MASE       ACF1
Training set -33.42686 134.653 118.8589 -2.025789 6.38553 0.8592207 -0.7441053

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 21153:  log likelihood=-38.39
AIC=78.78   AICc=79.78   BIC=78.58

Error measures:
                    ME    RMSE      MAE       MPE    MAPE      MASE       ACF1
Training set -33.42686 134.653 118.8589 -2.025789 6.38553 0.8592207 -0.7441053

Forecasts:
rownames(amr_f04) <- "amr_f04"

amr_m514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m514")

 ARIMA(0,1,0)                    : 80.43822
 ARIMA(0,1,0) with drift         : 82.89086
 ARIMA(0,1,1)                    : 85.35448
 ARIMA(0,1,1) with drift         : 92.87508
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 85.25797
 ARIMA(1,1,0) with drift         : 92.87029
 ARIMA(1,1,1)                    : 94.93816
 ARIMA(1,1,1) with drift         : 122.6887
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 94.65097
 ARIMA(2,1,0) with drift         : 122.7689
 ARIMA(2,1,1)                    : 124.598
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 23590:  log likelihood=-38.72
AIC=79.44   AICc=80.44   BIC=79.23

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -76.94743 142.1967 104.7669 -2.600088 3.547003 0.8610975 -0.2447804

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 23590:  log likelihood=-38.72
AIC=79.44   AICc=80.44   BIC=79.23

Error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -76.94743 142.1967 104.7669 -2.600088 3.547003 0.8610975 -0.2447804

Forecasts:
rownames(amr_m514) <- "amr_m514"

amr_f514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f514")

 ARIMA(0,1,0)                    : 81.77671
 ARIMA(0,1,0) with drift         : 85.53773
 ARIMA(0,1,1)                    : 85.39397
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 84.22718
 ARIMA(1,1,0) with drift         : 92.24655
 ARIMA(1,1,1)                    : 93.97957
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : Inf
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 29485:  log likelihood=-39.39
AIC=80.78   AICc=81.78   BIC=80.57

Training set error measures:
                  ME     RMSE     MAE       MPE    MAPE      MASE      ACF1
Training set -63.094 158.9745 120.906 -2.160652 3.97228 0.8605409 -0.510281

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 29485:  log likelihood=-39.39
AIC=80.78   AICc=81.78   BIC=80.57

Error measures:
                  ME     RMSE     MAE       MPE    MAPE      MASE      ACF1
Training set -63.094 158.9745 120.906 -2.160652 3.97228 0.8605409 -0.510281

Forecasts:
rownames(amr_f514) <- "amr_f514"

amr_m014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m014")

 ARIMA(0,1,0)                    : 84.45934
 ARIMA(0,1,0) with drift         : 87.54733
 ARIMA(0,1,1)                    : 89.18806
 ARIMA(0,1,1) with drift         : 97.48774
 ARIMA(0,1,2)                    : 99.10081
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 89.11319
 ARIMA(1,1,0) with drift         : 97.52031
 ARIMA(1,1,1)                    : 99.10554
 ARIMA(1,1,1) with drift         : 127.4837
 ARIMA(1,1,2)                    : 129.0897
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : Inf
 ARIMA(2,1,0) with drift         : 127.3181
 ARIMA(2,1,1)                    : 129.1039
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 46111:  log likelihood=-40.73
AIC=83.46   AICc=84.46   BIC=83.25

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -95.30943 198.8054 97.83343 -1.802642 1.847534 0.8645075 -0.1921184

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 46111:  log likelihood=-40.73
AIC=83.46   AICc=84.46   BIC=83.25

Error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE       ACF1
Training set -95.30943 198.8054 97.83343 -1.802642 1.847534 0.8645075 -0.1921184

Forecasts:
rownames(amr_m014) <- "amr_m014"

amr_f014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f014")

 ARIMA(0,1,0)                    : 81.98907
 ARIMA(0,1,0) with drift         : 85.52465
 ARIMA(0,1,1)                    : 86.57645
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 86.05811
 ARIMA(1,1,0) with drift         : 93.61714
 ARIMA(1,1,1)                    : 95.78387
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 95.5139
 ARIMA(2,1,0) with drift         : 123.5324
 ARIMA(2,1,1)                    : 125.511
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 30550:  log likelihood=-39.49
AIC=80.99   AICc=81.99   BIC=80.78

Training set error measures:
                  ME     RMSE     MAE       MPE     MAPE      MASE       ACF1
Training set -68.933 161.8208 117.067 -1.369866 2.307828 0.8629017 -0.5168809

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 30550:  log likelihood=-39.49
AIC=80.99   AICc=81.99   BIC=80.78

Error measures:
                  ME     RMSE     MAE       MPE     MAPE      MASE       ACF1
Training set -68.933 161.8208 117.067 -1.369866 2.307828 0.8629017 -0.5168809

Forecasts:
rownames(amr_f014) <- "amr_f014"

amr_m15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m15plus")

 ARIMA(0,1,0)                    : 121.5033
 ARIMA(0,1,0) with drift         : 121.2176
 ARIMA(0,1,1)                    : 124.9374
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : 133.9693
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 124.2796
 ARIMA(1,1,0) with drift         : 130.5986
 ARIMA(1,1,1)                    : 134.1506
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : 163.6115
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 134.0446
 ARIMA(2,1,0) with drift         : 160.3891
 ARIMA(2,1,1)                    : 164.0426
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0) with drift         

Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
         drift
      3600.333
s.e.  1236.437

sigma^2 estimated as 11010114:  log likelihood=-56.61
AIC=117.22   AICc=121.22   BIC=116.8

Training set error measures:
                   ME     RMSE      MAE        MPE     MAPE      MASE       ACF1
Training set 17.92894 2804.348 2433.643 -0.0223538 1.731512 0.6560275 -0.2334609

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: . 
ARIMA(0,1,0) with drift 

Coefficients:
         drift
      3600.333
s.e.  1236.437

sigma^2 estimated as 11010114:  log likelihood=-56.61
AIC=117.22   AICc=121.22   BIC=116.8

Error measures:
                   ME     RMSE      MAE        MPE     MAPE      MASE       ACF1
Training set 17.92894 2804.348 2433.643 -0.0223538 1.731512 0.6560275 -0.2334609

Forecasts:
rownames(amr_m15plus) <- "amr_m15plus"

amr_f15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f15plus")

 ARIMA(0,1,0)                    : 107.077
 ARIMA(0,1,0) with drift         : 111.465
 ARIMA(0,1,1)                    : 111.6485
 ARIMA(0,1,1) with drift         : Inf
 ARIMA(0,1,2)                    : Inf
 ARIMA(0,1,2) with drift         : Inf
 ARIMA(1,1,0)                    : 111.8425
 ARIMA(1,1,0) with drift         : 120.2414
 ARIMA(1,1,1)                    : 121.6324
 ARIMA(1,1,1) with drift         : Inf
 ARIMA(1,1,2)                    : Inf
 ARIMA(1,1,2) with drift         : Inf
 ARIMA(2,1,0)                    : 120.2146
 ARIMA(2,1,0) with drift         : 144.5938
 ARIMA(2,1,1)                    : Inf
 ARIMA(2,1,1) with drift         : Inf
 ARIMA(2,1,2)                    : Inf
 ARIMA(2,1,2) with drift         : Inf



 Best model: ARIMA(0,1,0)                    

Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 2e+06:  log likelihood=-52.04
AIC=106.08   AICc=107.08   BIC=105.87

Training set error measures:
                   ME     RMSE      MAE       MPE    MAPE      MASE       ACF1
Training set 388.1107 1309.374 1138.968 0.4951078 1.50792 0.8652579 -0.2900684

Forecast method: ARIMA(0,1,0)

Model Information:
Series: . 
ARIMA(0,1,0) 

sigma^2 estimated as 2e+06:  log likelihood=-52.04
AIC=106.08   AICc=107.08   BIC=105.87

Error measures:
                   ME     RMSE      MAE       MPE    MAPE      MASE       ACF1
Training set 388.1107 1309.374 1138.968 0.4951078 1.50792 0.8652579 -0.2900684

Forecasts:
rownames(amr_f15plus) <- "amr_f15plus"

amr_estimates <- rbind(amr_m04, amr_f04, amr_m514, amr_f514, amr_m014, amr_f014, amr_m15plus, amr_f15plus)

Combining the data for 2020 with the model estimates

##AFR difference calculations 
afr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AFR") %>% t() 
afr_2020 <- as.data.frame(afr_2020[c(3:10), ])

rownames(afr_2020) <- c("afr_m04", "afr_f04", "afr_m514", "afr_f514", "afr_m014", "afr_f014", "afr_m15plus", "afr_f15plus")
colnames(afr_2020) <- "notif_2020"

est_afr_2020 <- cbind(afr_2020, afr_estimates)
colnames(est_afr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_afr_2020$notif_2020 <- as.numeric(est_afr_2020$notif_2020)

dif_afr <- mutate(est_afr_2020, "Difference" = notif_2020 - est_2020, "afr_perc" = 100*(Difference/est_2020))

##SEA difference calculations 
sea_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "SEA") %>% t() 
sea_2020 <- as.data.frame(sea_2020[c(3:10), ])

rownames(sea_2020) <- c("sea_m04", "sea_f04", "sea_m514", "sea_f514", "sea_m014", "sea_f014", "sea_m15plus", "sea_f15plus")
colnames(sea_2020) <- "notif_2020"
est_sea_2020 <- cbind(sea_2020, sea_estimates)
colnames(est_sea_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_sea_2020$notif_2020 <- as.numeric(est_sea_2020$notif_2020)

dif_sea <- mutate(est_sea_2020, "Difference" = notif_2020 - est_2020, "sea_perc" = 100*(Difference/est_2020))

#WPR difference calculations 
wpr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "WPR") %>% t() 
wpr_2020 <- as.data.frame(wpr_2020[c(3:10), ])

rownames(wpr_2020) <- c("wpr_m04", "wpr_f04", "wpr_m514", "wpr_f514", "wpr_m014", "wpr_f014", "wpr_m15plus", "wpr_f15plus")
colnames(wpr_2020) <- "wpr_2020"
est_wpr_2020 <- cbind(wpr_2020, wpr_estimates)
colnames(est_wpr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_wpr_2020$notif_2020 <- as.numeric(est_wpr_2020$notif_2020)

dif_wpr <- mutate(est_wpr_2020, "Difference" = notif_2020 - est_2020, "wpr_perc" = 100*(Difference/est_2020))

#WPR difference calculations 
eur_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EUR") %>% t() 
eur_2020 <- as.data.frame(eur_2020[c(3:10), ])

rownames(eur_2020) <- c("eur_m04", "eur_f04", "eur_m514", "eur_f514", "eur_m014", "eur_f014", "eur_m15plus", "eur_f15plus")
colnames(eur_2020) <- "eur_2020"
est_eur_2020 <- cbind(eur_2020, eur_estimates)
colnames(est_eur_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_eur_2020$notif_2020 <- as.numeric(est_eur_2020$notif_2020)

dif_eur <- mutate(est_eur_2020, "Difference" = notif_2020 - est_2020, "eur_perc" = 100*(Difference/est_2020)) 

#AMR difference calculations
amr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AMR") %>% t() 
amr_2020 <- as.data.frame(amr_2020[c(3:10), ])

rownames(amr_2020) <- c("amr_m04", "amr_f04", "amr_m514", "amr_f514", "amr_m014", "amr_f014", "amr_m15plus", "amr_f15plus")
colnames(eur_2020) <- "amr_2020"
est_amr_2020 <- cbind(amr_2020, amr_estimates)
colnames(est_amr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_amr_2020$notif_2020 <- as.numeric(est_amr_2020$notif_2020)

dif_amr <- mutate(est_amr_2020, "Difference" = notif_2020 - est_2020, "amr_perc" = 100*(Difference/est_2020)) 

#EMR difference calculations
emr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EMR") %>% t() 
emr_2020 <- as.data.frame(emr_2020[c(3:10), ])

rownames(emr_2020) <- c("emr_m04", "emr_f04", "emr_m514", "emr_f514", "emr_m014", "emr_f014", "emr_m15plus", "emr_f15plus")
colnames(emr_2020) <- "emr_2020"
est_emr_2020 <- cbind(emr_2020, emr_estimates)
colnames(est_emr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_emr_2020$notif_2020 <- as.numeric(est_emr_2020$notif_2020)

dif_emr <- mutate(est_emr_2020, "Difference" = notif_2020 - est_2020, "emr_perc" = 100*(Difference/est_2020)) 

#Combined data frame with difference data 
combined_dif <- data.frame(dif_afr[, c(7,8)], dif_amr[,c(7,8)], dif_emr[, c(7,8)], dif_eur[,c(7:8)], dif_sea[,c(7:8)], dif_wpr[,c(7:8)])
combined_dif$group <- as.vector(rownames(combined_dif))
combined_dif$group <- c("m04", "f04", "m514", "f514", "m014", "f014", "m15plus", "f15plus")
rownames(combined_dif) <- c(1:8)
combined_dif <- combined_dif[, c(13,1:12)]

colnames(combined_dif) <- c("group", "AFR_dif", "AFR_perc", "AMR_dif", "AMR_perc", "EMR_dif", "EMR_perc", "EUR_dif", "EUR_perc", "SEA_dif", "SEA_perc", "WPR_dif", "WPR_perc")

#isolating only the percentage data to be able to plot a bar plot 
bar_data <- combined_dif[, c(1,3,5,7,9,11,13)]
bar_newgroup <- str_split_fixed(bar_data$group, "", 2)
bar_data2 <- cbind(bar_data, bar_newgroup)
colnames(bar_data2) <- c("group", "AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "sex", "age_group")
bar_data2 <- bar_data2[, c(9,8,2:7)]


piv_dif <- pivot_longer(bar_data2, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"), names_to = "g_whoregion", values_to = "value")

library(dplyr)
library(ggplot2)
piv_three <- filter(piv_dif, age_group != "014")

##Attempt at drawing plots  including 0-14 
arima_dif <- ggplot(piv_dif, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_dif$age_group) + geom_bar(stat = "identity", position = "dodge")

##Attempt excluding 0-14 
piv_copy <- piv_three
piv_copy$age_group <- factor(piv_copy$age_group, levels = c("04", "514", "15plus"))
arima_copy <- ggplot(piv_copy, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_copy$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_copy


arima_three <- ggplot(piv_three, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_three$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_three

NA
#Attempt at the forecasting models 
#Rearranging data for 0-4 males 

males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04)) 
piv_na <- pivot_wider(males_04, names_from = "year", values_from = "newrel_m04")
piv_na$`2020` <- NA
males_back <- pivot_longer(piv_na, cols = c(as.vector(colnames(piv_na[,2:9]))), names_to = "year")
males_back$year <- as.numeric(males_back$year)
males_no_pivot <- males_04
males_pivot <- pivot_wider(males_04, names_from = "g_whoregion", values_from = "newrel_m04")

select(afr_m04, "Point Forecast")

fore2020 <- function(df){
  a <- select(df, "Point Forecast")
  b <- "2020"
  colnames(a) <- "Number"
  rownames(a) <- NULL
  a$Year <- b
  return(a)
}

#males 04 
fore_afrm04 <- fore2020(afr_m04)
fore_amrm04 <- fore2020(amr_m04)
fore_emrm04 <- fore2020(emr_m04)
fore_eurm04 <- fore2020(eur_m04)
fore_seam04 <- fore2020(sea_m04)
fore_wprm04 <- fore2020(wpr_m04)

bound_fore <- cbind(fore_afrm04, fore_amrm04$Number, fore_emrm04$Number, fore_eurm04$Number, fore_seam04$Number, fore_wprm04$Number)

colnames(bound_fore) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_fore <- bound_fore[, c(2, 1, 3:7)]

#females04
fore_afrf04 <- fore2020(afr_f04)
fore_amrf04 <- fore2020(amr_f04)
fore_emrf04 <- fore2020(emr_f04)
fore_eurf04 <- fore2020(eur_f04)
fore_seaf04 <- fore2020(sea_f04)
fore_wprf04 <- fore2020(wpr_f04)

bound_f04 <- cbind(fore_afrf04, fore_amrf04$Number, fore_emrf04$Number, fore_eurf04$Number, fore_seaf04$Number, fore_wprf04$Number)

colnames(bound_f04) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f04 <- bound_f04[, c(2, 1, 3:7)]

#males 514 
fore_afrm514 <- fore2020(afr_m514)
fore_amrm514 <- fore2020(amr_m514)
fore_emrm514 <- fore2020(emr_m514)
fore_eurm514 <- fore2020(eur_m514)
fore_seam514 <- fore2020(sea_m514)
fore_wprm514 <- fore2020(wpr_m514)

bound_m514 <- cbind(fore_afrm514, fore_amrm514$Number, fore_emrm514$Number, fore_eurm514$Number, fore_seam514$Number, fore_wprm514$Number)

colnames(bound_m514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m514 <- bound_m514[, c(2, 1, 3:7)]

#females 514 
fore_afrf514 <- fore2020(afr_f514)
fore_amrf514 <- fore2020(amr_f514)
fore_emrf514 <- fore2020(emr_f514)
fore_eurf514 <- fore2020(eur_f514)
fore_seaf514 <- fore2020(sea_f514)
fore_wprf514 <- fore2020(wpr_f514)

bound_f514 <- cbind(fore_afrf514, fore_amrf514$Number, fore_emrf514$Number, fore_eurf514$Number, fore_seaf514$Number, fore_wprf514$Number)

colnames(bound_f514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f514 <- bound_f514[, c(2, 1, 3:7)]

#males 15plus 
fore_afrm15plus <- fore2020(afr_m15plus)
fore_amrm15plus <- fore2020(amr_m15plus)
fore_emrm15plus <- fore2020(emr_m15plus)
fore_eurm15plus <- fore2020(eur_m15plus)
fore_seam15plus <- fore2020(sea_m15plus)
fore_wprm15plus <- fore2020(wpr_m15plus)

bound_m15plus <- cbind(fore_afrm15plus, fore_amrm15plus$Number, fore_emrm15plus$Number, fore_eurm15plus$Number, fore_seam15plus$Number, fore_wprm15plus$Number)

colnames(bound_m15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m15plus <- bound_m15plus[, c(2, 1, 3:7)]

#females 15plus 
fore_afrf15plus <- fore2020(afr_f15plus)
fore_amrf15plus <- fore2020(amr_f15plus)
fore_emrf15plus <- fore2020(emr_f15plus)
fore_eurf15plus <- fore2020(eur_f15plus)
fore_seaf15plus <- fore2020(sea_f15plus)
fore_wprf15plus <- fore2020(wpr_f15plus)

bound_f15plus <- cbind(fore_afrf15plus, fore_amrf15plus$Number, fore_emrf15plus$Number, fore_eurf15plus$Number, fore_seaf15plus$Number, fore_wprf15plus$Number)

colnames(bound_f15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f15plus <- bound_f15plus[, c(2, 1, 3:7)]

Attempt 2 at creating the time series analyses

#males04 plot
males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04)) 
gg_males04 <- ggplot(males_04, aes(x = year, y = newrel_m04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 27043, col = "red") + geom_point(x = 2020, y = 2062, col = "brown") + geom_point(x = 2020, y = 17243.17, col = "green") + geom_point(x = 2020, y = 1465, col = "turquoise2") + geom_point(x = 2020, y = 58951.83, col = "blue") + geom_point(x = 2020, y = 12558, col = "pink") + scale_y_continuous(limits = c(0, 60000)) + labs(title = "Notifications Time Series for Males 0-4 yrs")

#females 04 plot
females_04 <- select(perc_world, c(g_whoregion, year, newrel_f04)) 
gg_females04 <- ggplot(females_04, aes(x = year, y = newrel_f04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 23790, col = "red") + geom_point(x = 2020, y = 1776, col = "brown") + geom_point(x = 2020, y = 13286.17, col = "green") + geom_point(x = 2020, y = 1269, col = "turquoise2") + geom_point(x = 2020, y = 43694, col = "blue") + geom_point(x = 2020, y = 10140, col = "pink") + scale_y_continuous(limits = c(0, 50000)) + labs(title = "Notifications Time Series for Females 0-4 yrs")

#males 514 plot 
males_514 <- select(perc_world, c(g_whoregion, year, newrel_m514)) 
gg_males514 <- ggplot(males_514, aes(x = year, y = newrel_m514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32352, col = "red") + geom_point(x = 2020, y = 2826, col = "brown") + geom_point(x = 2020, y = 18620, col = "green") + geom_point(x = 2020, y = 3057, col = "turquoise2") + geom_point(x = 2020, y = 82535, col = "blue") + geom_point(x = 2020, y = 19606, col = "pink") + scale_y_continuous(limits = c(0, 90000)) + labs(title = "Notifications Time Series for Males 5-14 yrs")

#females514
females_514 <- select(perc_world, c(g_whoregion, year, newrel_f514)) 
gg_females514 <- ggplot(females_514, aes(x = year, y = newrel_f514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32411, col = "red") + geom_point(x = 2020, y = 2897, col = "brown") + geom_point(x = 2020, y = 21632, col = "green") + geom_point(x = 2020, y = 2953, col = "turquoise2") + geom_point(x = 2020, y = 87086, col = "blue") + geom_point(x = 2020, y = 17500, col = "pink") + scale_y_continuous(limits = c(0, 100000)) + labs(title = "Notifications Time Series for Females 5-14 yrs")

#males 15 plus plot 
males_15plus <- select(perc_world, c(g_whoregion, year, newrel_m15plus)) 
gg_males15plus <- ggplot(males_15plus, aes(x = year, y = newrel_m15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 661953, col = "red") + geom_point(x = 2020, y = 154305.3, col = "brown") + geom_point(x = 2020, y = 226294, col = "green") + geom_point(x = 2020, y = 129819.7, col = "turquoise2") + geom_point(x = 2020, y = 1931341, col = "blue") + geom_point(x = 2020, y = 891849, col = "pink") + scale_y_continuous(limits = c(0, 2000000)) + labs(title = "Notifications Time Series for Males 15plus yrs")

#females 15 plus plot 
females_15plus <- select(perc_world, c(g_whoregion, year, newrel_f15plus)) 
gg_females15plus <- ggplot(females_15plus, aes(x = year, y = newrel_f15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 426790, col = "red") + geom_point(x = 2020, y = 77417, col = "brown") + geom_point(x = 2020, y = 201386, col = "green") + geom_point(x = 2020, y = 66222.5, col = "turquoise2") + geom_point(x = 2020, y = 1182645, col = "blue") + geom_point(x = 2020, y = 435651, col = "pink") + scale_y_continuous(limits = c(0, 1200000)) + labs(title = "Notifications Time Series for Females 15plus yrs")

Creating notification graphs for entire world

#Putting together notification data for years 2013 to 2020
world_data <- world_2013[,-c(1:3)] %>% group_by(year) %>% na.omit()
tot_m04 <- summarise(world_data, total = sum(newrel_m04))
tot_m04 %<>% as.data.frame()
colnames(tot_m04) <- c("year", "m04")

tot_f04 <- summarise(world_data, total = sum(newrel_f04))
tot_f04 %<>% as.data.frame()
colnames(tot_f04) <- c("year", "f04")

tot_m514 <- summarise(world_data, total = sum(newrel_m514))
tot_m514 %<>% as.data.frame()
colnames(tot_m514) <- c("year", "m514")

tot_f514 <- summarise(world_data, total = sum(newrel_f514))
tot_f514 %<>% as.data.frame()
colnames(tot_f514) <- c("year", "f514")

tot_m15plus <- summarise(world_data, total = sum(newrel_m15plus))
tot_m15plus %<>% as.data.frame()
colnames(tot_m15plus) <- c("year", "m15plus")

tot_f15plus <- summarise(world_data, total = sum(newrel_f15plus))
tot_f15plus %<>% as.data.frame()
colnames(tot_f15plus) <- c("year", "f15plus")

all_tots <- cbind(tot_m04, tot_f04$f04, tot_m514$m514, tot_f514$f514, tot_m15plus$m15plus, tot_f15plus$f15plus)
colnames(all_tots) <- c("year", "case_m04", "case_f04", "case_m514", "case_f514", "case_m15plus", "case_f15plus")

all_tots <- mutate(all_tots, "all04" = case_m04 + case_f04, "all514" = case_m514 + case_f514, "all15plus" = case_m15plus + case_f15plus, "all_male" = case_m04 + case_m514 + case_m15plus, "all_female" = case_f04 + case_f514 + case_f15plus, "all_cases" = all_male + all_female)

all_copy <- all_tots
all_copy04 <- all_copy[, c(1:3)] 
colnames(all_copy04) <- c("year", "male", "female")
all_copy04 <- pivot_longer(all_copy04, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy514 <- all_copy[, c(1,4,5)] 
colnames(all_copy514) <- c("year", "male", "female")
all_copy514 <- pivot_longer(all_copy514, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy15plus <- all_copy[, c(1,6,7)] 
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy15plus <- all_copy[, c(1,6,7)] 
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")

###Drawing the plots 
world_04 <- ggplot(all_copy04, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 0-4 yrs from 2013 to 2020")

world_514 <- ggplot(all_copy514, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 5-14 yrs from 2013 to 2020")

world_15plus <- ggplot(all_copy15plus, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 15plus yrs from 2013 to 2020")

Repeating the continental difference between actual and predicted and including column for overall

---
title: "All Countries Regional Data"
output: html_notebook
---

AIM: Create region by region analysis of male vs female for each age groups (0-4, 5-14, 0-14 and 15 plus) for all countries separated by region

```{r}
setwd("~/Desktop/AFP/modV3/data")
library(dplyr)
library(tidyverse)
library(tidyr)
library(forecast)
library(fpp2)
library(stringr)
library(magrittr)
library(ggplot2)

world <- read_csv("TB_notifications.csv")
tidyworld <- world[, c(1,3,5,6,100,103,104,113,115,118,119,128)]
world_2013 <- tidyworld %>% filter(year > 2012)
```

Organising the data by g_whoregion

```{r}
grouped_2013 <- group_by(world_2013, g_whoregion) %>% filter(year == 2013) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2013)

grouped_2014 <- group_by(world_2013, g_whoregion) %>% filter(year == 2014) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2014)

grouped_2015 <- group_by(world_2013, g_whoregion) %>% filter(year == 2015) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2015)

grouped_2016 <- group_by(world_2013, g_whoregion) %>% filter(year == 2016) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2016)

grouped_2017 <- group_by(world_2013, g_whoregion) %>% filter(year == 2017) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2017)

grouped_2018 <- group_by(world_2013, g_whoregion) %>% filter(year == 2018) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2018)

grouped_2019 <- group_by(world_2013, g_whoregion) %>% filter(year == 2019) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2019)

grouped_2020 <- group_by(world_2013, g_whoregion) %>% filter(year == 2020) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2020)

bind_world <- rbind(grouped_2013, grouped_2014, grouped_2015, grouped_2016, grouped_2017, grouped_2018, grouped_2019, grouped_2020)

bind_world <- bind_world[, c(1, 10, 2:9)]

arrange_world <- arrange(bind_world, g_whoregion)

##Including columns for total values 

mutate_world <- mutate(arrange_world, "newrel_tot04" = newrel_m04 + newrel_f04, "newrel_tot514" = newrel_m514 + newrel_f514, "newrel_tot014" = newrel_m014 + newrel_f014, "newrel_tot15plus" = newrel_m15plus + newrel_f15plus)

perc_world <- mutate(mutate_world, "perc_014" = 100 * (newrel_tot014/(newrel_tot014 + newrel_tot15plus)), "perc_youngkids" = 100 * (newrel_tot04/newrel_tot014))



```

Creating graphs for male vs female for 0-4 by g_whoregion

```{r}
afr_04 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(afr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AFR")

amr_04 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(amr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AMR")

emr_04 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(emr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EMR")

eur_04 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(eur_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EUR")

sea_04 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m04, newrel_f04) %>% 
  t() 
colnames(sea_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,60000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in SEA")

wpr_04 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(wpr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in WPR")


```
Creating graphs for notifications in 5-14 years by g_whoregion

```{r}
afr_514 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,40000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-514 years in AFR")

amr_514 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,4000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in AMR")

emr_514 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(emr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EMR")

eur_514 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m514, newrel_f514) %>% 
  t()
colnames(eur_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,5000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EUR")

sea_514 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(sea_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,100000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in SEA")

wpr_514 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m514, newrel_f514) %>% 
  t()
colnames(wpr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in WPR")
```
Creating plot for 0-14 

```{r}
afr_014 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(afr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,70000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AFR")

amr_014 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(amr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,6000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AMR")

emr_014 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(emr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EMR")

eur_014 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(eur_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,8000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EUR")

sea_014 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(sea_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,150000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in SEA")

wpr_014 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(wpr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in WPR")
```
Creating plot for 15 plus 

```{r}
afr_15plus <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(afr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in AFR")

amr_15plus <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t()
colnames(amr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,300000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15 plus years in AMR")

emr_15plus <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(emr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EMR")

eur_15plus <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(eur_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EUR")

sea_15plus <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(sea_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,2000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in SEA")

wpr_15plus <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(wpr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in WPR")
```
Plotting total notifications over time 

```{r}
tot_world <- mutate(perc_world, "newrel_mtot" = newrel_m014 + newrel_m15plus, "newrel_ftot" = newrel_f014 + newrel_f15plus, "TOT" = newrel_mtot + newrel_ftot)

afr_tot <- tot_world %>% filter(g_whoregion == "AFR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(afr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AFR")

amr_tot <- tot_world %>% filter(g_whoregion == "AMR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t()
colnames(amr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AMR")

emr_tot <- tot_world %>% filter(g_whoregion == "EMR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(emr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EMR")

eur_tot <- tot_world %>% filter(g_whoregion == "EUR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t()
colnames(eur_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EUR")

sea_tot <- tot_world %>% filter(g_whoregion == "SEA") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(sea_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in SEA")

wpr_tot <- tot_world %>% filter(g_whoregion == "WPR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(wpr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in WPR")

```
```{r}
##Attempt at creating a forecast 

filt_afr04 <- perc_world %>% filter(g_whoregion == "AFR") %>% filter(year != 2020)

ts_afr04 <- ts(filt_afr04[, c(3)], start = 2013, frequency = 1)

arima_afr04 <- auto.arima(ts_afr04, d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
print(summary(arima_afr04))
checkresiduals(arima_afr04)

fcst_afrm04 <- forecast(arima_afr04, h = 1)
autoplot(fcst)
sum_afrm04 <- print(summary(fcst))

####Attempt at creating function to produce predictions 

out_2020 <- function(df = tot_world, region){
        df %>% filter(year != 2020) %>% filter(g_whoregion == region) %>% return() }

arima_TB <- function(df = tot_world, group) {
        data1 <- df %>% select(group) %>% ts(start = 2013, frequency = 1) %>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE) 
        print(summary(data1))
        FCST <- forecast(data1, h = 1)
        autoplot(FCST)
        print(summary(FCST))}

##AFRICA ESTIMATES 

afr_m04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m04") 
rownames(afr_m04) <- "afr_m04"

afr_f04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f04")
rownames(afr_f04) <- "afr_f04"

afr_m514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m514")
rownames(afr_m514) <- "afr_m514"

afr_f514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f514")
rownames(afr_f514) <- "afr_f514"

afr_m014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m014")
rownames(afr_m014) <- "afr_m014"

afr_f014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f014")
rownames(afr_f014) <- "afr_f014"

afr_m15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m15plus")
rownames(afr_m15plus) <- "afr_m15plus"

afr_f15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f15plus")
rownames(afr_f15plus) <- "afr_f15plus"

afr_estimates <- rbind(afr_m04, afr_f04, afr_m514, afr_f514, afr_m014, afr_f014, afr_m15plus, afr_f15plus)


#SEA predictions 
sea_m04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m04") 
rownames(sea_m04) <- "sea_m04"

sea_f04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f04")
rownames(sea_f04) <- "sea_f04"

sea_m514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m514")
rownames(sea_m514) <- "sea_m514"

sea_f514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f514")
rownames(sea_f514) <- "sea_f514"

sea_m014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m014")
rownames(sea_m014) <- "sea_m014"

sea_f014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f014")
rownames(sea_f014) <- "sea_f014"

sea_m15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m15plus")
rownames(sea_m15plus) <- "sea_m15plus"

sea_f15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f15plus")
rownames(sea_f15plus) <- "sea_f15plus"

sea_estimates <- rbind(sea_m04, sea_f04, sea_m514, sea_f514, sea_m014, sea_f014, sea_m15plus, sea_f15plus)

#WPR estimates 

wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04") 
rownames(wpr_m04) <- "wpr_m04"

wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
rownames(wpr_f04) <- "wpr_f04"

wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
rownames(wpr_m514) <- "wpr_m514"

wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
rownames(wpr_f514) <- "wpr_f514"

wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
rownames(wpr_m014) <- "wpr_m014"

wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
rownames(wpr_f014) <- "wpr_f014"

wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
rownames(wpr_m15plus) <- "wpr_m15plus"

wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
rownames(wpr_f15plus) <- "wpr_f15plus"

wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)

#EUR Estimates

eur_m04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m04") 
rownames(eur_m04) <- "eur_m04"

eur_f04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f04")
rownames(eur_f04) <- "eur_f04"

eur_m514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m514")
rownames(eur_m514) <- "eur_m514"

eur_f514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f514")
rownames(eur_f514) <- "eur_f514"

eur_m014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m014")
rownames(eur_m014) <- "eur_m014"

eur_f014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f014")
rownames(eur_f014) <- "eur_f014"

eur_m15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m15plus")
rownames(eur_m15plus) <- "eur_m15plus"

eur_f15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f15plus")
rownames(eur_f15plus) <- "eur_f15plus"

eur_estimates <- rbind(eur_m04, eur_f04, eur_m514, eur_f514, eur_m014, eur_f014, eur_m15plus, eur_f15plus)

#AMR Estimates

emr_m04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m04") 
rownames(emr_m04) <- "emr_m04"

emr_f04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f04")
rownames(emr_f04) <- "emr_f04"

emr_m514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m514")
rownames(emr_m514) <- "emr_m514"

emr_f514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f514")
rownames(emr_f514) <- "emr_f514"

emr_m014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m014")
rownames(emr_m014) <- "emr_m014"

emr_f014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f014")
rownames(emr_f014) <- "emr_f014"

emr_m15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m15plus")
rownames(emr_m15plus) <- "emr_m15plus"

emr_f15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f15plus")
rownames(emr_f15plus) <- "emr_f15plus"

emr_estimates <- rbind(emr_m04, emr_f04, emr_m514, emr_f514, emr_m014, emr_f014, emr_m15plus, emr_f15plus)

#WPR Estimates

wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04") 
rownames(wpr_m04) <- "wpr_m04"

wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
rownames(wpr_f04) <- "wpr_f04"

wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
rownames(wpr_m514) <- "wpr_m514"

wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
rownames(wpr_f514) <- "wpr_f514"

wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
rownames(wpr_m014) <- "wpr_m014"

wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
rownames(wpr_f014) <- "wpr_f014"

wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
rownames(wpr_m15plus) <- "wpr_m15plus"

wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
rownames(wpr_f15plus) <- "wpr_f15plus"

wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)

#AMR Estimates

amr_m04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m04") 
rownames(amr_m04) <- "amr_m04"

amr_f04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f04")
rownames(amr_f04) <- "amr_f04"

amr_m514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m514")
rownames(amr_m514) <- "amr_m514"

amr_f514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f514")
rownames(amr_f514) <- "amr_f514"

amr_m014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m014")
rownames(amr_m014) <- "amr_m014"

amr_f014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f014")
rownames(amr_f014) <- "amr_f014"

amr_m15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m15plus")
rownames(amr_m15plus) <- "amr_m15plus"

amr_f15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f15plus")
rownames(amr_f15plus) <- "amr_f15plus"

amr_estimates <- rbind(amr_m04, amr_f04, amr_m514, amr_f514, amr_m014, amr_f014, amr_m15plus, amr_f15plus)

```
Combining the data for 2020 with the model estimates 

```{r}
##AFR difference calculations 
afr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AFR") %>% t() 
afr_2020 <- as.data.frame(afr_2020[c(3:10), ])

rownames(afr_2020) <- c("afr_m04", "afr_f04", "afr_m514", "afr_f514", "afr_m014", "afr_f014", "afr_m15plus", "afr_f15plus")
colnames(afr_2020) <- "notif_2020"

est_afr_2020 <- cbind(afr_2020, afr_estimates)
colnames(est_afr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_afr_2020$notif_2020 <- as.numeric(est_afr_2020$notif_2020)

dif_afr <- mutate(est_afr_2020, "Difference" = notif_2020 - est_2020, "afr_perc" = 100*(Difference/est_2020))

##SEA difference calculations 
sea_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "SEA") %>% t() 
sea_2020 <- as.data.frame(sea_2020[c(3:10), ])

rownames(sea_2020) <- c("sea_m04", "sea_f04", "sea_m514", "sea_f514", "sea_m014", "sea_f014", "sea_m15plus", "sea_f15plus")
colnames(sea_2020) <- "notif_2020"
est_sea_2020 <- cbind(sea_2020, sea_estimates)
colnames(est_sea_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_sea_2020$notif_2020 <- as.numeric(est_sea_2020$notif_2020)

dif_sea <- mutate(est_sea_2020, "Difference" = notif_2020 - est_2020, "sea_perc" = 100*(Difference/est_2020))

#WPR difference calculations 
wpr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "WPR") %>% t() 
wpr_2020 <- as.data.frame(wpr_2020[c(3:10), ])

rownames(wpr_2020) <- c("wpr_m04", "wpr_f04", "wpr_m514", "wpr_f514", "wpr_m014", "wpr_f014", "wpr_m15plus", "wpr_f15plus")
colnames(wpr_2020) <- "wpr_2020"
est_wpr_2020 <- cbind(wpr_2020, wpr_estimates)
colnames(est_wpr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_wpr_2020$notif_2020 <- as.numeric(est_wpr_2020$notif_2020)

dif_wpr <- mutate(est_wpr_2020, "Difference" = notif_2020 - est_2020, "wpr_perc" = 100*(Difference/est_2020))

#EUR difference calculations 
eur_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EUR") %>% t() 
eur_2020 <- as.data.frame(eur_2020[c(3:10), ])

rownames(eur_2020) <- c("eur_m04", "eur_f04", "eur_m514", "eur_f514", "eur_m014", "eur_f014", "eur_m15plus", "eur_f15plus")
colnames(eur_2020) <- "eur_2020"
est_eur_2020 <- cbind(eur_2020, eur_estimates)
colnames(est_eur_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_eur_2020$notif_2020 <- as.numeric(est_eur_2020$notif_2020)

dif_eur <- mutate(est_eur_2020, "Difference" = notif_2020 - est_2020, "eur_perc" = 100*(Difference/est_2020)) 

#AMR difference calculations
amr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AMR") %>% t() 
amr_2020 <- as.data.frame(amr_2020[c(3:10), ])

rownames(amr_2020) <- c("amr_m04", "amr_f04", "amr_m514", "amr_f514", "amr_m014", "amr_f014", "amr_m15plus", "amr_f15plus")
colnames(eur_2020) <- "amr_2020"
est_amr_2020 <- cbind(amr_2020, amr_estimates)
colnames(est_amr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_amr_2020$notif_2020 <- as.numeric(est_amr_2020$notif_2020)

dif_amr <- mutate(est_amr_2020, "Difference" = notif_2020 - est_2020, "amr_perc" = 100*(Difference/est_2020)) 

#EMR difference calculations
emr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EMR") %>% t() 
emr_2020 <- as.data.frame(emr_2020[c(3:10), ])

rownames(emr_2020) <- c("emr_m04", "emr_f04", "emr_m514", "emr_f514", "emr_m014", "emr_f014", "emr_m15plus", "emr_f15plus")
colnames(emr_2020) <- "emr_2020"
est_emr_2020 <- cbind(emr_2020, emr_estimates)
colnames(est_emr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_emr_2020$notif_2020 <- as.numeric(est_emr_2020$notif_2020)

dif_emr <- mutate(est_emr_2020, "Difference" = notif_2020 - est_2020, "emr_perc" = 100*(Difference/est_2020)) 

#Combined data frame with difference data 
combined_dif <- data.frame(dif_afr[, c(7,8)], dif_amr[,c(7,8)], dif_emr[, c(7,8)], dif_eur[,c(7:8)], dif_sea[,c(7:8)], dif_wpr[,c(7:8)])
combined_dif$group <- as.vector(rownames(combined_dif))
combined_dif$group <- c("m04", "f04", "m514", "f514", "m014", "f014", "m15plus", "f15plus")
rownames(combined_dif) <- c(1:8)
combined_dif <- combined_dif[, c(13,1:12)]

colnames(combined_dif) <- c("group", "AFR_dif", "AFR_perc", "AMR_dif", "AMR_perc", "EMR_dif", "EMR_perc", "EUR_dif", "EUR_perc", "SEA_dif", "SEA_perc", "WPR_dif", "WPR_perc")

#isolating only the percentage data to be able to plot a bar plot 
bar_data <- combined_dif[, c(1,3,5,7,9,11,13)]
bar_newgroup <- str_split_fixed(bar_data$group, "", 2)
bar_data2 <- cbind(bar_data, bar_newgroup)
colnames(bar_data2) <- c("group", "AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "sex", "age_group")
bar_data2 <- bar_data2[, c(9,8,2:7)]


piv_dif <- pivot_longer(bar_data2, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"), names_to = "g_whoregion", values_to = "value")

library(dplyr)
library(ggplot2)
piv_three <- filter(piv_dif, age_group != "014")

##Attempt at drawing plots  including 0-14 
arima_dif <- ggplot(piv_dif, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_dif$age_group) + geom_bar(stat = "identity", position = "dodge")

##Attempt excluding 0-14 
piv_copy <- piv_three
piv_copy$age_group <- factor(piv_copy$age_group, levels = c("04", "514", "15plus"))
arima_copy <- ggplot(piv_copy, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_copy$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_copy

arima_three <- ggplot(piv_three, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_three$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_three

``` 



```{r}
#Attempt at the forecasting models 
#Rearranging data for 0-4 males 

males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04)) 
piv_na <- pivot_wider(males_04, names_from = "year", values_from = "newrel_m04")
piv_na$`2020` <- NA
males_back <- pivot_longer(piv_na, cols = c(as.vector(colnames(piv_na[,2:9]))), names_to = "year")
males_back$year <- as.numeric(males_back$year)
males_no_pivot <- males_04
males_pivot <- pivot_wider(males_04, names_from = "g_whoregion", values_from = "newrel_m04")

select(afr_m04, "Point Forecast")

fore2020 <- function(df){
  a <- select(df, "Point Forecast")
  b <- "2020"
  colnames(a) <- "Number"
  rownames(a) <- NULL
  a$Year <- b
  return(a)
}

#males 04 
fore_afrm04 <- fore2020(afr_m04)
fore_amrm04 <- fore2020(amr_m04)
fore_emrm04 <- fore2020(emr_m04)
fore_eurm04 <- fore2020(eur_m04)
fore_seam04 <- fore2020(sea_m04)
fore_wprm04 <- fore2020(wpr_m04)

bound_fore <- cbind(fore_afrm04, fore_amrm04$Number, fore_emrm04$Number, fore_eurm04$Number, fore_seam04$Number, fore_wprm04$Number)

colnames(bound_fore) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_fore <- bound_fore[, c(2, 1, 3:7)]

#females04
fore_afrf04 <- fore2020(afr_f04)
fore_amrf04 <- fore2020(amr_f04)
fore_emrf04 <- fore2020(emr_f04)
fore_eurf04 <- fore2020(eur_f04)
fore_seaf04 <- fore2020(sea_f04)
fore_wprf04 <- fore2020(wpr_f04)

bound_f04 <- cbind(fore_afrf04, fore_amrf04$Number, fore_emrf04$Number, fore_eurf04$Number, fore_seaf04$Number, fore_wprf04$Number)

colnames(bound_f04) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f04 <- bound_f04[, c(2, 1, 3:7)]

#males 514 
fore_afrm514 <- fore2020(afr_m514)
fore_amrm514 <- fore2020(amr_m514)
fore_emrm514 <- fore2020(emr_m514)
fore_eurm514 <- fore2020(eur_m514)
fore_seam514 <- fore2020(sea_m514)
fore_wprm514 <- fore2020(wpr_m514)

bound_m514 <- cbind(fore_afrm514, fore_amrm514$Number, fore_emrm514$Number, fore_eurm514$Number, fore_seam514$Number, fore_wprm514$Number)

colnames(bound_m514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m514 <- bound_m514[, c(2, 1, 3:7)]

#females 514 
fore_afrf514 <- fore2020(afr_f514)
fore_amrf514 <- fore2020(amr_f514)
fore_emrf514 <- fore2020(emr_f514)
fore_eurf514 <- fore2020(eur_f514)
fore_seaf514 <- fore2020(sea_f514)
fore_wprf514 <- fore2020(wpr_f514)

bound_f514 <- cbind(fore_afrf514, fore_amrf514$Number, fore_emrf514$Number, fore_eurf514$Number, fore_seaf514$Number, fore_wprf514$Number)

colnames(bound_f514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f514 <- bound_f514[, c(2, 1, 3:7)]

#males 15plus 
fore_afrm15plus <- fore2020(afr_m15plus)
fore_amrm15plus <- fore2020(amr_m15plus)
fore_emrm15plus <- fore2020(emr_m15plus)
fore_eurm15plus <- fore2020(eur_m15plus)
fore_seam15plus <- fore2020(sea_m15plus)
fore_wprm15plus <- fore2020(wpr_m15plus)

bound_m15plus <- cbind(fore_afrm15plus, fore_amrm15plus$Number, fore_emrm15plus$Number, fore_eurm15plus$Number, fore_seam15plus$Number, fore_wprm15plus$Number)

colnames(bound_m15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m15plus <- bound_m15plus[, c(2, 1, 3:7)]

#females 15plus 
fore_afrf15plus <- fore2020(afr_f15plus)
fore_amrf15plus <- fore2020(amr_f15plus)
fore_emrf15plus <- fore2020(emr_f15plus)
fore_eurf15plus <- fore2020(eur_f15plus)
fore_seaf15plus <- fore2020(sea_f15plus)
fore_wprf15plus <- fore2020(wpr_f15plus)

bound_f15plus <- cbind(fore_afrf15plus, fore_amrf15plus$Number, fore_emrf15plus$Number, fore_eurf15plus$Number, fore_seaf15plus$Number, fore_wprf15plus$Number)

colnames(bound_f15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f15plus <- bound_f15plus[, c(2, 1, 3:7)]


```

Attempt 2 at creating the time series analyses 

```{r}
#males04 plot
males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04)) 
gg_males04 <- ggplot(males_04, aes(x = year, y = newrel_m04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 27043, col = "red") + geom_point(x = 2020, y = 2062, col = "brown") + geom_point(x = 2020, y = 17243.17, col = "green") + geom_point(x = 2020, y = 1465, col = "turquoise2") + geom_point(x = 2020, y = 58951.83, col = "blue") + geom_point(x = 2020, y = 12558, col = "pink") + scale_y_continuous(limits = c(0, 60000)) + labs(title = "Notifications Time Series for Males 0-4 yrs")

#females 04 plot
females_04 <- select(perc_world, c(g_whoregion, year, newrel_f04)) 
gg_females04 <- ggplot(females_04, aes(x = year, y = newrel_f04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 23790, col = "red") + geom_point(x = 2020, y = 1776, col = "brown") + geom_point(x = 2020, y = 13286.17, col = "green") + geom_point(x = 2020, y = 1269, col = "turquoise2") + geom_point(x = 2020, y = 43694, col = "blue") + geom_point(x = 2020, y = 10140, col = "pink") + scale_y_continuous(limits = c(0, 50000)) + labs(title = "Notifications Time Series for Females 0-4 yrs")

#males 514 plot 
males_514 <- select(perc_world, c(g_whoregion, year, newrel_m514)) 
gg_males514 <- ggplot(males_514, aes(x = year, y = newrel_m514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32352, col = "red") + geom_point(x = 2020, y = 2826, col = "brown") + geom_point(x = 2020, y = 18620, col = "green") + geom_point(x = 2020, y = 3057, col = "turquoise2") + geom_point(x = 2020, y = 82535, col = "blue") + geom_point(x = 2020, y = 19606, col = "pink") + scale_y_continuous(limits = c(0, 90000)) + labs(title = "Notifications Time Series for Males 5-14 yrs")

#females514
females_514 <- select(perc_world, c(g_whoregion, year, newrel_f514)) 
gg_females514 <- ggplot(females_514, aes(x = year, y = newrel_f514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32411, col = "red") + geom_point(x = 2020, y = 2897, col = "brown") + geom_point(x = 2020, y = 21632, col = "green") + geom_point(x = 2020, y = 2953, col = "turquoise2") + geom_point(x = 2020, y = 87086, col = "blue") + geom_point(x = 2020, y = 17500, col = "pink") + scale_y_continuous(limits = c(0, 100000)) + labs(title = "Notifications Time Series for Females 5-14 yrs")

#males 15 plus plot 
males_15plus <- select(perc_world, c(g_whoregion, year, newrel_m15plus)) 
gg_males15plus <- ggplot(males_15plus, aes(x = year, y = newrel_m15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 661953, col = "red") + geom_point(x = 2020, y = 154305.3, col = "brown") + geom_point(x = 2020, y = 226294, col = "green") + geom_point(x = 2020, y = 129819.7, col = "turquoise2") + geom_point(x = 2020, y = 1931341, col = "blue") + geom_point(x = 2020, y = 891849, col = "pink") + scale_y_continuous(limits = c(0, 2000000)) + labs(title = "Notifications Time Series for Males 15plus yrs")

#females 15 plus plot 
females_15plus <- select(perc_world, c(g_whoregion, year, newrel_f15plus)) 
gg_females15plus <- ggplot(females_15plus, aes(x = year, y = newrel_f15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 426790, col = "red") + geom_point(x = 2020, y = 77417, col = "brown") + geom_point(x = 2020, y = 201386, col = "green") + geom_point(x = 2020, y = 66222.5, col = "turquoise2") + geom_point(x = 2020, y = 1182645, col = "blue") + geom_point(x = 2020, y = 435651, col = "pink") + scale_y_continuous(limits = c(0, 1200000)) + labs(title = "Notifications Time Series for Females 15plus yrs")

```
Creating notification graphs for entire world 

```{r}
#Putting together notification data for years 2013 to 2020
world_data <- world_2013[,-c(1:3)] %>% group_by(year) %>% na.omit()
tot_m04 <- summarise(world_data, total = sum(newrel_m04))
tot_m04 %<>% as.data.frame()
colnames(tot_m04) <- c("year", "m04")

tot_f04 <- summarise(world_data, total = sum(newrel_f04))
tot_f04 %<>% as.data.frame()
colnames(tot_f04) <- c("year", "f04")

tot_m514 <- summarise(world_data, total = sum(newrel_m514))
tot_m514 %<>% as.data.frame()
colnames(tot_m514) <- c("year", "m514")

tot_f514 <- summarise(world_data, total = sum(newrel_f514))
tot_f514 %<>% as.data.frame()
colnames(tot_f514) <- c("year", "f514")

tot_m15plus <- summarise(world_data, total = sum(newrel_m15plus))
tot_m15plus %<>% as.data.frame()
colnames(tot_m15plus) <- c("year", "m15plus")

tot_f15plus <- summarise(world_data, total = sum(newrel_f15plus))
tot_f15plus %<>% as.data.frame()
colnames(tot_f15plus) <- c("year", "f15plus")

all_tots <- cbind(tot_m04, tot_f04$f04, tot_m514$m514, tot_f514$f514, tot_m15plus$m15plus, tot_f15plus$f15plus)
colnames(all_tots) <- c("year", "case_m04", "case_f04", "case_m514", "case_f514", "case_m15plus", "case_f15plus")

all_tots <- mutate(all_tots, "all04" = case_m04 + case_f04, "all514" = case_m514 + case_f514, "all15plus" = case_m15plus + case_f15plus, "all_male" = case_m04 + case_m514 + case_m15plus, "all_female" = case_f04 + case_f514 + case_f15plus, "all_cases" = all_male + all_female)

all_copy <- all_tots
all_copy04 <- all_copy[, c(1:3)] 
colnames(all_copy04) <- c("year", "male", "female")
all_copy04 <- pivot_longer(all_copy04, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy514 <- all_copy[, c(1,4,5)] 
colnames(all_copy514) <- c("year", "male", "female")
all_copy514 <- pivot_longer(all_copy514, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy15plus <- all_copy[, c(1,6,7)] 
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy15plus <- all_copy[, c(1,6,7)] 
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copyall <- all_copy[, c(1,11,12)] 
colnames(all_copyall) <- c("year", "male", "female")
all_copyall <- pivot_longer(all_copyall, cols = c(male, female), names_to = "sex", values_to = "cases")

###Drawing the plots 
world_04 <- ggplot(all_copy04, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 0-4 yrs from 2013 to 2020")

world_514 <- ggplot(all_copy514, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 5-14 yrs from 2013 to 2020")

world_15plus <- ggplot(all_copy15plus, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 15plus yrs from 2013 to 2020")

world_all <- ggplot(all_copyall, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst All Ages from 2013 to 2020")

```

Repeating the continental difference between actual and predicted and including column for overall 

```{r}
#extracting data for whole world 
summed_m04 <- tot_world %>% group_by(year) %>% na.omit()
summed_m04 <- summarise(summed_m04, total = sum(newrel_m04))
colnames(summed_m04) <- c("year", "m04")

summed_f04 <- tot_world %>% group_by(year) %>% na.omit()
summed_f04 <- summarise(summed_f04, total = sum(newrel_f04))
colnames(summed_f04) <- c("year", "f04")

summed_m514 <- tot_world %>% group_by(year) %>% na.omit()
summed_m514 <- summarise(summed_m514, total = sum(newrel_m514))
colnames(summed_m514) <- c("year", "m514")

summed_f514 <- tot_world %>% group_by(year) %>% na.omit()
summed_f514 <- summarise(summed_f514, total = sum(newrel_f514))
colnames(summed_f514) <- c("year", "f514")

summed_m15plus <- tot_world %>% group_by(year) %>% na.omit()
summed_m15plus <- summarise(summed_m15plus, total = sum(newrel_m15plus))
colnames(summed_m15plus) <- c("year", "m15plus")

summed_f15plus <- tot_world %>% group_by(year) %>% na.omit()
summed_f15plus <- summarise(summed_f15plus, total = sum(newrel_f15plus))
colnames(summed_f15plus) <- c("year", "f15plus")

all_summed <- cbind(summed_m04, summed_f04$f04, summed_m514$m514, summed_f514$f514, summed_m15plus$m15plus, summed_f15plus$f15plus)
colnames(all_summed) <- c("year", "m04", "f04", "m514", "f514", "m15plus", "f15plus")

##Attempt at creating forecast 
arima_allm04 <- all_summed[-8, 2]
ts_arimam04 <- ts(arima_allm04, start = 2013, frequency = 1)
ts_arimam04 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m04 <- forecast(ts_arimam04, h = 1)
autoplot(forecast_m04)
sum_m04 <- print(summary(forecast_m04))

arima_allf04 <- all_summed[-8, 3]
ts_arimaf04 <- ts(arima_allf04, start = 2013, frequency = 1)
ts_arimaf04 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f04 <- forecast(ts_arimaf04, h = 1)
autoplot(forecast_f04)
sum_f04 <- print(summary(forecast_f04))

arima_allm514 <- all_summed[-8, 4]
ts_arimam514 <- ts(arima_allm514, start = 2013, frequency = 1)
ts_arimam514 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m514 <- forecast(ts_arimam514, h = 1)
autoplot(forecast_m514)
sum_m514 <- print(summary(forecast_m514))

arima_allf514 <- all_summed[-8, 5]
ts_arimaf514 <- ts(arima_allf514, start = 2013, frequency = 1)
ts_arimaf514 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f514 <- forecast(ts_arimaf514, h = 1)
autoplot(forecast_f514)
sum_f514 <- print(summary(forecast_f514))

arima_allm15plus <- all_summed[-8, 6]
ts_arimam15plus <- ts(arima_allm15plus, start = 2013, frequency = 1)
ts_arimam15plus %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m15plus <- forecast(ts_arimam15plus, h = 1)
autoplot(forecast_m15plus)
sum_m15plus <- print(summary(forecast_m15plus))

arima_allf15plus <- all_summed[-8, 7]
ts_arimaf15plus <- ts(arima_allf15plus, start = 2013, frequency = 1)
ts_arimaf15plus %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f15plus <- forecast(ts_arimaf15plus, h = 1)
autoplot(forecast_f15plus)
sum_f15plus <- print(summary(forecast_f15plus))

##Creating single data frame with best estimates for each group 
best_estimates <- cbind(sum_m04$`Point Forecast`, sum_f04$`Point Forecast`, sum_m514$`Point Forecast`, sum_f514$`Point Forecast`, sum_m15plus$`Point Forecast`, sum_f15plus$`Point Forecast`)
colnames(best_estimates) <- c("m04", "f04", "m514", "f514", "m15plus", "f15plus")

all_compared <- rbind(all_summed[8,-1], best_estimates)
rownames(all_compared) <- c("Actual", "Predicted")

all_differences <- t(all_compared)
all_differences %<>% as.data.frame()
all_differences <- mutate(all_differences, Percentage = 100*((Actual-Predicted)/Predicted))


sex <- c("m", "f")
add_col1 <- cbind(all_differences, sex)
age_group <- c("04", "04", "514", "514", "15plus", "15plus")
add_col2 <- cbind(add_col1, age_group)
g_whoregion <- "ALL"
add_col3 <- cbind(add_col2, g_whoregion)

#Ready to merge 
ready_all <- add_col3[, c(5,4,6,3)]
colnames(ready_all)[colnames(ready_all) == "Percentage"] <- "value"

piv_four <- rbind(piv_three, ready_all)
piv_four <- piv_four %>% arrange(age_group) %>% group_by(age_group) %>% arrange(sex)
piv_four$g_whoregion <- factor(piv_four$g_whoregion, levels = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "ALL"))

arima_four <- ggplot(piv_four, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_four$age_group) + geom_bar(stat = "identity", position = "dodge") + theme(axis.text.x = element_text(angle = 90)) + theme(legend.title = element_blank()) + xlab("Region") + ylab("Percentage Difference Between Actual and Predicted Cases in 2020") + labs(title = "Difference Between Actual and Predicted Notifications in 2020")

arima_four 
```


